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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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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.

Keywords:

autonomous vehicles computer vision deep learning mobile robotics perspective transformation pothole detection

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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.