Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation

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Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation

Saeed, Nausheen (author): Högskolan Dalarna,Mikrodataanalys

Alam, Moudud, 1976- (thesis advisor): Högskolan Dalarna,Statistik

Nyberg, Roger G., Doctor of Philosophy (thesis advisor): Högskolan Dalarna,Informatik

Lavesson, Niklas, Professor (opponent): Blekinge Institute of Technology

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(creator_code:org_t)

ISBN 9789188679598
Borlänge : Dalarna University, 2024
English.
Series: Dalarna Doctoral Dissertations ; 29

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Doctoral thesis (other academic/artistic)

Abstract Subject headings

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.

Subject headings

TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Infrastructure Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)

Keyword

Gravel road condition assessment
Loose gravel
Sound analysis
Machine learning
Image analysis
Audio analysis
Image and audio data fusion

Publication and Content Type

vet (subject category)
dok (subject category)

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Infrastructure E ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Reliability and ...

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