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Computer Vision Based Pothole Detection under Challenging Conditions.

Boris Bučko¹, Eva Lieskovská¹, Katarína Zábovská¹

¹University Science Park UNIZA, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia.

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|November 26, 2022

Summary

Early pothole detection using computer vision models like You Look Only Once version 3 (Yolo v3) is crucial for road safety. This study evaluates Yolo v3

Keywords:

Yolo v3 adverse conditions pavement distress pothole detection

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Area of Science:

Computer Vision and Machine Learning
Road Infrastructure Monitoring
Transportation Engineering

Background:

Potholes and road cracks pose significant risks to vehicles and road safety.
Early detection of road defects is essential for timely maintenance and accident prevention.
Adverse lighting and weather conditions challenge the performance of automated visual detection systems.

Purpose of the Study:

To investigate the impact of adverse environmental conditions on pothole detection accuracy.
To evaluate the effectiveness of the You Look Only Once version 3 (Yolo v3) model for pothole detection under varying conditions.
To establish a benchmark for pothole detection performance in challenging scenarios.

Main Methods:

Development of a specialized dataset featuring road images captured under diverse light and weather conditions.
Implementation and experimentation with the You Look Only Once version 3 (Yolo v3) object detection model.
Comparative analysis incorporating Sparse R-CNN to assess performance variations.

Main Results:

The study provides a detailed analysis of pothole detection performance under adverse conditions, a previously under-explored area.
Yolo v3 demonstrates competitive performance despite challenging environmental factors.
The created dataset serves as a valuable resource for future research in robust road defect detection.

Conclusions:

Yolo v3 remains a viable and efficient architecture for pothole detection, even with limited hardware.
Understanding the influence of adverse conditions is key to improving the reliability of automated road monitoring systems.
Further research can build upon these findings to enhance the robustness of computer vision models for road maintenance.