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Gravel road classification based on loose gravel using transfer learning
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
2022 (English)In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, p. 1-8Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2022. p. 1-8
Keywords [en]
Convolutional neural networks; transfer learning; deep learning; loose gravel; gravel road maintenance; road condition assessment
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:du-43181DOI: 10.1080/10298436.2022.2138879ISI: 000882702900001Scopus ID: 2-s2.0-85141687244OAI: oai:DiVA.org:du-43181DiVA, id: diva2:1710431
Available from: 2022-11-13 Created: 2022-11-13 Last updated: 2024-01-30
In thesis
1. 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, Moudud

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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