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Updated: Aug 19, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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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.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
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 v3adverse conditionspavement distresspothole 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.