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Leveraging Perspective Transformation for Enhanced Pothole Detection in Autonomous Vehicles
Abdalmalek Abu-Raddaha1, Zaid A El-Shair1, Samir Rawashdeh1
1Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Journal of Imaging
|September 27, 2024
Summary
Detecting distant potholes is crucial for road safety. This study uses perspective transformation to improve pothole detection, especially for far-away hazards, significantly boosting accuracy.
Area of Science:
- Computer Vision
- Road Safety Engineering
Background:
- Degraded road conditions and limited reaction time contribute to accidents.
- Early detection of potholes is vital for preventing vehicle damage and ensuring safety.
Purpose of the Study:
- To introduce a novel method for enhancing pothole detection, particularly for distant potholes.
- To improve the visibility and clarity of potholes using perspective transformation.
Main Methods:
- Automatic identification of the road region of interest (ROI).
- Calculation of corner points to generate a perspective transformation matrix.
- Application of the matrix to images and bounding box labels for dataset enhancement.
Main Results:
- A 43% improvement in average precision (AP) using YOLOv5-small.
- Significant detection improvements for near (34%), medium (63%), and far (194%) potholes.
- Demonstrated effectiveness of perspective transformation for distant pothole detection.
Conclusions:
- Perspective transformation is a novel and effective technique for enhancing pothole detection, especially for distant objects.
- This method significantly improves the performance of object detection networks in real-world road conditions.
- The approach offers a promising solution for improving road safety through advanced computer vision techniques.
Keywords:
autonomous vehiclescomputer visiondeep learningmobile roboticsperspective transformationpothole detectionMore Related Videos
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