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Classification of the Acoustics of Loose Gravel
Dalarna University, School of Information and Engineering, Microdata Analysis. (Microdata analysis)
Dalarna University, School of Information and Engineering, Informatics.ORCID iD: 0000-0003-4812-4988
Dalarna University, School of Information and Engineering, Statistics.ORCID iD: 0000-0002-3183-3756
School of Information Technology, Halmstad University.ORCID iD: 0000-0001-7713-8292
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 14, article id 4944Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2021. Vol. 21, no 14, article id 4944
Keywords [en]
gravel roads; loose gravel; ensemble bagged trees; sound analysis; road maintenance; GoogLeNet
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:du-37811DOI: 10.3390/s21144944ISI: 000677020000001PubMedID: 34300684Scopus ID: 2-s2.0-85110519300OAI: oai:DiVA.org:du-37811DiVA, id: diva2:1582598
Available from: 2021-08-02 Created: 2021-08-02 Last updated: 2024-01-30Bibliographically approved
In thesis
1. Automated Gravel Road Condition Assessment: A Case Study of Assessing Loose Gravel using Audio Data
Open this publication in new window or tab >>Automated Gravel Road Condition Assessment: A Case Study of Assessing Loose Gravel using Audio Data
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Gravel roads connect sparse populations and provide highways for agriculture and the transport of forest goods. Gravel roads are an economical choice where traffic volume is low. In Sweden, 21% of all public roads are state-owned gravel roads, covering over 20,200 km. In addition, there are some 74,000 km of gravel roads and 210,000 km of forest roads that are owned by the private sector. The Swedish Transport Administration (Trafikverket) rates the condition of gravel roads according to the severity of irregularities (e.g. corrugations and potholes), dust, loose gravel, and gravel cross-sections. This assessment is carried out during the summertime when roads are free of snow. One of the essential parameters for gravel road assessment is loose gravel. Loose gravel can cause a tire to slip, leading to a loss of driver control.  Assessment of gravel roads is carried out subjectively by taking images of road sections and adding some textual notes. A cost-effective, intelligent, and objective method for road assessment is lacking. Expensive methods, such as laser profiler trucks, are available and can offer road profiling with high accuracy. These methods are not applied to gravel roads, however, because of the need to maintain cost-efficiency. 

In this thesis, we explored the idea that, in addition to machine vision, we could also use machine hearing to classify the condition of gravel roads in relation to loose gravel. Several suitable classical supervised learning and convolutional neural networks (CNN) were tested. When people drive on gravel roads, they can make sense of the road condition by listening to the gravel hitting the bottom of the car. The more we hear gravel hitting the bottom of the car, the more we can sense that there is a lot of loose gravel and, therefore, the road might be in a bad condition. Based on this idea, we hypothesized that machines could also undertake such a classification when trained with labeled sound data. Machines can identify gravel and non-gravel sounds. In this thesis, we used traditional machine learning algorithms, such as support vector machines (SVM), decision trees, and ensemble classification methods. We also explored CNN for classifying spectrograms of audio sounds and images in gravel roads. Both supervised learning and CNN were used, and results were compared for this study. In classical algorithms, when compared with other classifiers, ensemble bagged tree (EBT)-based classifiers performed best for classifying gravel and non-gravel sounds. EBT performance is also useful in reducing the misclassification of non-gravel sounds. The use of CNN also showed a 97.91% accuracy rate. Using CNN makes the classification process more intuitive because the network architecture takes responsibility for selecting the relevant training features. Furthermore, the classification results can be visualized on road maps, which can help road monitoring agencies assess road conditions and schedule maintenance activities for a particular road.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2021
Series
Dalarna Licentiate Theses ; 17
Keywords
Gravel roads, road maintenance, convolutional neural network (CNN), SVM, decision trees, ensemble bagged trees, GoogLeNet, ResNet50, ResNet18, sound analysis
National Category
Infrastructure Engineering Computer and Information Sciences
Identifiers
urn:nbn:se:du-36402 (URN)978-91-88679-14-7 (ISBN)
Presentation
2021-05-28, a digital seminar and room 244, Campus Borlänge, 13:00 (English)
Opponent
Supervisors
Note

Due to unforeseen circumstances the seminar was postponed from May 7 to 28, as duly stated in the new posting page.

Available from: 2021-04-08 Created: 2021-04-06 Last updated: 2023-04-14Bibliographically approved
2. Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation
Open this publication in new window or tab >>Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A well-maintained road network is essential for sustainable economic development, providing vital transportation routes for goods and services while connecting communities. Sweden's public road network includes a significant portion of gravel roads, particularly cost-effective for less populated areas with lower traffic volumes. However, gravel roads deteriorate quickly, leading to accidents, environmental pollution, and vehicle tire wear when not adequately maintained. The Swedish Road Administration Authority (Trafikverket) assesses gravel road conditions using subjective methods, analysing images taken during snow-free periods. Due to cost constraints, this labour-intensive process is prone to errors and lacks advanced techniques like road profilometers.

This thesis explores the field of assessing gravel road conditions. It commences with a comprehensive review of manual gravel road assessment methods employed globally and existing data-driven smart methods. Subsequently, it harnesses machine hearing and machine vision techniques, primarily focusing on enhancing road condition classification by integrating sound and image data.

The research examines sound data collected from gravel roads, exploring machine learning algorithms for loose gravel conditions classification with potential road maintenance and monitoring implications. Another crucial aspect involves applying machine vision to categorise image data from gravel roads. The study introduces an innovative approach using publicly available resources like Google Street View for image data collection, demonstrating machine vision's adaptability in assessing road conditions.

The research also compares machine learning methods with manual human classification, specifically regarding sound data. Automated approaches consistently outperform manual methods, providing more reliable results. Furthermore, the thesis investigates combining audio and image data to classify road conditions, particularly loose gravel scenarios. Early feature fusion using pre-trained models significantly improves classifier accuracy.

The research proposes using cost-effective devices like mobile phones with AI applications attached to car windshields to collect audio and visual data on gravel road conditions. This approach can provide more accurate and efficient data collection, resulting in real-time mapping of road conditions over considerable distances. Such information can benefit drivers, travellers, and road maintenance agencies by identifying problematic areas with loose gravel, enabling targeted and efficient maintenance efforts, and minimising disruptions to traffic flow during maintenance operations.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2024
Series
Dalarna Doctoral Dissertations ; 29
Keywords
Gravel road condition assessment, Loose gravel, Sound analysis, Machine learning, Image analysis, Audio analysis, Image and audio data fusion
National Category
Infrastructure Engineering Reliability and Maintenance
Identifiers
urn:nbn:se:du-47915 (URN)978-91-88679-59-8 (ISBN)
Public defence
2024-03-28, room Clas Ohlson, campus Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2024-02-23 Created: 2024-01-25 Last updated: 2024-02-23Bibliographically approved

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Saeed, NausheenNyberg, Roger G.Alam, MoududDougherty, MarkJomaa, DialaRebreyend, Pascal

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