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This study investigates the combination of audio and image data to classify road conditions, particularly focusingon loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturinggravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep ConvolutionalNetworks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forestclassifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevelprocessing with logical disjunction and conjunction gates (AND and OR) in combination with individualclassifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121),demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusionmethod enhances adaptability by compensating for limitations in one modality with information from the other.Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. Thismethod can help to identify loose gravel on gravel roads, substantially improving road safety and implementing aprecise maintenance strategy through a data-driven approach.

Maintaining gravel roads is crucial, as loose gravel poses safety risks and increases vehicle costs. Current methods used by the Swedish road administration, Trafikverket, are subjective and time-consuming. Road agencies need a cost-effective, efficient, and unbiased approach to assess gravel road conditions. Studies show human ratings are error-prone and inconsistent. This study aims to develop an automatic method for estimating loose gravel using audio recordings from inside a vehicle, capturing the sound of gravel hitting the car's bottom. These recordings were classified into four classes based on Trafikverket regulations. Sound features were extracted and analysed using supervised machine-learning methods. The Multilayer Perceptron (MLP) achieved the highest classification accuracy of 0.96, with an F1 score, recall, and precision of 0.97. Results indicate that audio data can effectively classify loose gravel conditions.

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.

This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.

Introduction

Chronic musculoskeletal pain is a highly prevalent, complex and distressing condition that may negatively affect all domains of life. In view of an active inference framework, and resting on the concept of allostasis, human movement per se becomes a prerequisite for health and well-being while chronic pain becomes a sign of a system unable to attenuate an allostatic load. Previous studies on different subgroups of chronic pain conditions have demonstrated alterations in gait kinematics and muscle activity, indicating shared disturbances in the motor system from long-term allostatic load. We hypothesise that such alterations exist in heterogenous populations with chronic musculoskeletal pain, and that exposure to acute and controlled exercise may attenuate these alterations. Therefore, the main aim of this study is to investigate the acute effects of exercise on gait kinematics and activity of the back and neck muscles during diverse walking conditions in patients with chronic musculoskeletal pain compared with a reference sample consisting of healthy participants.

Methods and analysis

This two-sample two-armed parallel randomised controlled laboratory trial will include 40 participants with chronic musculoskeletal pain (>3 months) and 40 healthy participants. Participants will be randomly allocated to either 30 min of aerobic exercise or rest. Primary outcomes are gait kinematics (walking speed, step frequency, stride length, lumbar rotation, gait stability) and muscular activity (spatial and temporal) of the back and neck during diverse walking conditions. Secondary outcomes are variability of gait kinematics and muscle activity and subjective pain ratings assessed regularly during the trial.

Ethics and dissemination

The study has been approved by the Regional Ethics Review Board in Uppsala, Sweden (#2018/307). Findings will be disseminated via conference presentations, publications in peer-reviewed journals and engagement with patient support groups and clinicians.

Trial registration number

NCT03882333.

Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.

Road maintenance agencies subjectively assess loose gravel as one of the parameters for determininggravel road conditions. This study aims to evaluate the performance of deep learning-based pretrainednetworks in rating gravel road images according to classical methods as done by humanexperts. The dataset consists of images of gravel roads extracted from self-recorded videos andimages extracted from Google Street View. The images were labelled manually, referring to thestandard images as ground truth defined by the Road Maintenance Agency in Sweden (Trafikverket).The dataset was then partitioned in a ratio of 60:40 for training and testing. Various pre-trainedmodels for computer vision tasks, namely Resnet18, Resnet50, Alexnet, DenseNet121, DenseNet201,and VGG-16, were used in the present study. The last few layers of these models were replaced toaccommodate new image categories for our application. All the models performed well, with anaccuracy of over 92%. The results reveal that the pre-trained VGG-16 with transfer learning exhibitedthe best performance in terms of accuracy and F1-score compared to other proposed models.

Purpose

Current online business development redistributes last-mile logistics (LML) from consumer to retailer and producer. This paper identifies how empirical LML research has used and defined logistic performance measures for key grocery industry actors. Using a multi-actor perspective on logistic performance, the authors discuss coordination issues important for optimising LML at system level.

Design/methodology/approach

A semi-systematic literature review of 85 publications was conducted to analyse performance measurements used for effectiveness and efficiency, and for which actors.

Findings

Few empirical LML studies exist examining coordination between key actors or on system level. Most studies focus on logistic performance measurements for retailers and/or consumers, not producers. Key goals and resource utilisations lack research, including all key actors and system-level coordination.

Research limitations/implications

Current LML performance research implies a risk for sub-optimisation. Through expanding on efficiency and effectiveness interplay at system level and introducing new research perspectives, the review highlights the need to revaluate single-actor, single-measurement studies.

Practical implications

No established scientific guidelines exist for solving LML optimisation in the grocery industry. For managers, it is important to thoroughly consider efficiency and effectiveness in LML execution, coordination and collaboration among key actors, avoiding sub-optimisations for business and sustainability.

Originality/value

The study contributes to current knowledge by reviewing empirical research on LML performance in the grocery sector, showing how previous research disregards the importance of multiple actors and coordination of actors, efficiency and effectiveness.

We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-making methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

In early spring 2020, Covid-19 was categorized as a pandemic and has since infected several millions of people in many countries and claimed hundreds of thousands of lives. Various strict strategies and prevention measures, such as curfews and lockdowns of cities or entire countries, have been enforced by governments to mitigate the spread of the virus. While the results of the aforementioned enforced measures deemed promising for some countries, the same could not be said about others. This paper serves as an initial analysis of the effect of government enforced strategies and safety measures on the transmission of Covid-19. We propose a three-stage periodic model: The rise stage, plateau stage, and decline stage, to describe the changes of the spread of Covid-19. The results show a positive and constructive answer to our proposed three-stage model. © 2020 ACM.

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Non-gravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non-gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

Last mile handover is the most problematic phase in the delivery process, while real-time communication and dynamic scheduling are major problem areas associated with attended last mile handovers. These problem areas need to be addressed holistically to facilitate efficient last mile handovers. The aim of this paper is to report the design and functionalities of a decision-support system which holistically addresses these problem areas. The functionalities of decision-support system which addresses dynamic scheduling and real-time communication problem areas are discussed using case studies. We conclude that a holistic decision-support system with multiparty communication among the stakeholders facilitates improving customer satisfaction, business opportunities and reducing operational costs for logistics companies.

The last mile handover is assumed to be the most problematic part in the delivery process and the costs can go upto 50% of the total logistic cost. Real time consumer communication and dynamic scheduling are the major problem areas associated with effective attended last mile handovers. Therefore, aim of this paper is to report the design and development of a holistic decision support system’s functionalities which simultaneously addresses real time consumer communication and dynamic scheduling. A decision support system was designed and developed based on workshops, expert group interviews and its functionalities were proposed with the use cases. A survey was conducted with consumers of a retailer where majority of the consumers accepted the use of mobile communication devices to enable real time communication and alternate handover location which improves customer satisfaction and facilitates to avoid missed handovers. A pilot test was performed where routing distance was reduced with the use of optimized handover routes. However the improvement is subjected to the experience of driver and real time traffic conditions. We conclude that a holistic decision support system with multi-party communication among the stakeholders facilitates in reducing operational costs for logistic companies and improving customer satisfaction and business opportunities.

A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Mathematical analytical modeling and computer simulation of the physiological system is a complex problem with great number of variables and equations. The objective of this research is to describe the insulin-glucose subsystem using multi-agent modeling based on intelligence agents. Such an approach makes the modeling process easier and clearer to understand; moreover, new agents can be added or removed more easily to any investigations. The Stolwijk-Hardy mathematical model is used in two ways firstly by simulating the analytical model and secondly by dividing up the same model into several agents in a multiagent system. In the proposed approach a multi-agent system was used to build a model for glycemic homeostasis. Agents were used to represent the selected elements of the human body that play an active part in this process. The experiments conducted show that the multi-agent model has good temporal stability with the implemented behaviors, and good reproducibility and stability of the results. It has also shown that no matter what the order of communication between agents, the value of the result of the simulation was not affected. The results obtained from using the framework of multi-agent system actions were consistent with the results obtained with insulin-glucose models using analytical modeling.

This study has investigated the quality and reliability of manual assessments on railway embankments within the domain of railway maintenance. Manually inspecting vegetation on railway embankments is slow and time consuming. Maintenance personnel also require extensive knowledge of the plant species, ecology and bio-diversity to be able to recommend appropriate maintenance action. The overall objective of the study is to investigate the reliable nature of manual inspection routines in favour an automatic approach. Visual estimates of plant cover reported by domain experts’ have been studied on two separate railway sections in Sweden. The first study investigated visual estimates using aerial foliar cover (AFC) and sub-plot frequency (SF) methods to assess the plant cover on a railway section in Oxberg, Alvdalsbanan, Sweden. The second study investigated visual estimates using aerial canopy cover method on a railway section outside Vetlanda, Sweden. Visual estimates of the domain experts were recorded and analysis-of-variance (ANOVA) tests on the mean estimates were investigated to see whether if there were disagreements between the raters’. ICC(2, 1) was used to study the differences between the estimates. Results achieved in this work indicate statistically significant differences in the mean estimates of cover (p < 0.05) reported by the domain experts on both the occasions.

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment.

Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body.

A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways.

The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process.

Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms.

The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Vegetation growing on railway trackbeds and embankments can present several potential problems. Consequently, such vegetation iscontrolled through various maintenance procedures. In order to investigate the extent of maintenance needed, one of the first steps in anymaintenance procedure is to monitor or inspect the railway section in question. Monitoring is often carried out manually by sending out inspectorsor by watching recorded video clips of the section in question.To facilitate maintenance planning, the ability to assess the extent of vegetation becomes important. This paper investigates the reliability ofhuman assessments of vegetation on railway trackbeds.In this study, five maintenance engineers made independent visual estimates of vegetation cover and counted the number of plant clusters fromimages.The test results showed an inconsistency between the raters when it came to visually estimating plant cover and counting plant clusters. The resultsshowed that caution should be exercised when interpreting individual raters’ assessments of vegetation.

National Railway Administrations in Northern Europe do not employ systematic procedures in monitoring the current state of vegetation to form the basis of maintenance decision making. Current day vegetation maintenance is largely based on human visual estimates. This paper investigates a machine vision (MV) approach to be able to automatically quantify the amount of vegetation on a given railway section. An investigation assessing the reliability of human estimates is also conducted along the same railway section.A machine vision algorithm was developed and implemented. Initially, the algorithm determines a region of interest (ROI), i.e. the desired monitored area in each collected image. This ROI is dependent on fixed objects in the image, namely the two rails. When the rails are found the algorithm will compute the ROI, which is predetermined by e.g. the railway administrator. After this, a perspective projection correction will be made, and the vegetation will be segmented. Cover is reported as a percentage of the total ROI for each image. Results: The machine vision algorithm is capable of processing 98% of the images. Failure in the remaining 2% of cases is attributed to the algorithms' inability in find the rails within the image. Analysis of variance tests were conducted to compare the observers cover assessments in sample plots. Upon comparing the observers plot wise mean estimates with the machine vision output, results show that the human visual estimates do not correlate with the results reported by the machine vision output. As such, the result indicates that it is very hard to fit human estimates by regression with the machine vision result. Additionally the results show that humans are not in agreement with each other, and often are exaggerating the extent of vegetation cover compared to the machine vision output.The investigation shows that one should be very careful when trusting/interpreting human visual estimates. In conclusion, based on the results, the automated machine vision solution is proposed as complementing, or replacing, manual human inspections serving as a base for vegetation control decisions. Impact: By objectively measuring the quantity of vegetation, the maintenance planning and procurement can be effectively improved over time. A machine vision approach for condition monitoring of vegetation will enable condition based maintenance with prior consideration on issues mainly relevant to vegetation type, quantity and biodiversity.

Current day vegetation assessments within railway maintenance are (to a large extent) carried out manually. This study has investigated the reliability of such manual assessments by taking three non-domain experts into account. Thirty-five track images under different conditions were acquired for the purpose. For each image, the raters’ were asked to estimate the cover of woody plants, herbs and grass separately (in %) using methods such as aerial canopy cover, aerial foliar cover and sub-plot frequency. Visual estimates of raters’ were recorded and analysis-of-variance tests on the mean cover estimates were investigated to see whether if there were disagreements between the raters’.  In tra-correl ation coefficient was used to study the differences between the estimates. Results achieved in this work revealed that seven out of the nine analysis-of-variance tests conducted in this study have demonstrated significant difference in the mean estimates of cover (p < 0.05).

En miljöanpassad och resurseffektiv hantering av ogräs i spår ställer krav på god kunskap om vegetationsförhållanden för att behovsanpassa bekämpningsinsatserna. En automatiserad registrering skulle kunna utgöra ett komplement till dagens manuella inspektioner och skulle över tiden helt eller delvis kunna ersätta dessa. En utmaning är att hitta en metod som ger en rimlig upplösning i informationen som samlas in, så att den kan hanteras rationellt av berörda aktörer och samtidigt utgöra ett beslutsunderlag med tillräcklig precision. Projektet studerade två automatiserade metoder som kan vara aktuella för Trafikverket att använda i framtiden: 1) Machine vision metoden utnyttjar kamerasensorer för att känna av sin omgivning i det synliga respektive nära infraröda spektrumet. 2) N-sensorn sänder ut ljus inom det område som reflekteras av växternas klorofyll. Mängden klorofyll ger ett mätvärde som kan korreleras till biomassan. Valet av teknik beror på vad informationen ska användas till. Om syftet är att översiktligt kartlägga vegetationsförekomst i spår, för att planera åtgärder för underhåll, kan N-sensortekniken vara lämplig. Om man över ytan och tiden vill övervaka och kartlägga aktuell och precis vegetationsstatus, för att kunna bekämpa utvald vegetation med rätt insats, är machine vision tekniken bättre lämpad. Såväl machine vision metoden som N-sensortekniken bygger på registrering av data tillsammans med en GPS-positionering. På sikt kan denna information läggas i databaser som är direkt åtkomliga för berörda organisationer och t o m online i fält under eller i samband med en bekämpningsåtgärd. De två teknikerna jämfördes med manuella (visuella) skattningar av ogräsförekomsten. Den visuella skattningen av yttäckningsgrad av ogräs i fält skiljde sig statistiskt mellan olika bedömare. När det gäller att uppskatta frekvensen (antalet) vedartade växter (träd och buskar) inom provytorna så var observatörerna relativt överens. Samma person är ofta konsekvent i sitt bedömande, men att jämföra med andra personers bedömning kan ge missvisande resultat. Systemet för användning av informationen om ogräsförekomst behöver utvecklas som helhet. Tröskelvärden för hur mycket ogräs som kan tolereras på olika typer av spår/driftsplatser är en viktig komponent i ett sådant system. Klassificeringssystemet ska kunna hantera de krav som ställs för att säkerställa banans kvalitet och olika förutsättningar som trafikförhållanden, platsgivna förutsättningar för banan och vegetationens egenskaper. Projektet rekommenderar Trafikverket att: diskutera hur tröskelvärden för vegetationsförekomst på spår kan fastställas  genomföra registrering av vegetationsförekomst över längre och fler sträckor med en eller flera av de metoder som studerats i projektet inleda införande av system som effektivt kopplar informationen om vegetation till position inkludera förekomst av vegetation i den registrering som idag sker av spårens (banans) tekniska kvalitet och ansluta datamaterialet till övriga underhållsrelaterade databaser inrätta ett antal representativa ytor där ogräsfloran på spåren regelbundet inventeras och mäts för att få en bild av den långsiktiga utveckling som grund för säkrare prognoser för vegetationsutveckling säkerställa att nödvändiga utbildningsinsatser genomförs

This paper investigates problems concerning vegetation along railways and proposes automatic means of detecting ground vegetation. Digital images of railway embankments have been acquired and used for the purpose. The current work mainly proposes two algorithms to be able to achieve automation. Initially a vegetation detection algorithm has been investigated for the purpose of detecting vegetation. Further a rail detection algorithm that is capable of identifying the rails and eventually the valid sampling area has been investigated. Results achieved in the current work report satisfactory (qualitative) detection rates.

The presence of vegetation on railway tracks (amongst other issues) threatens track safety and longevity. However, vegetation inspections in Sweden (and elsewhere in the world) are currently being carried out manually. Manually inspecting vegetation is very slow and time consuming. Maintaining an even quality standard is also very difficult. A machine vision-based approach is therefore proposed to emulate the visual abilities of the human inspector. Work aimed at detecting vegetation on railway tracks has been split into two main phases. The first phase is aimed at detecting vegetation on the tracks using appropriate image analysis techniques. The second phase is aimed at detecting the rails in the image to determine the cover of vegetation that is present between the rails as opposed to vegetation present outside the rails. Results achieved in the current work indicate that the machine vision approach has performed reasonably well in detecting the presence/absence of vegetation on railway tracks when compared with a human operator.

The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections