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RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection.

Yutian Jiang¹, Haotian Yan¹, Yiru Zhang²

¹College of Transportation, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

A new deep learning model, Road Defect Detection YOLOv5 (RDD-YOLOv5), significantly improves road crack detection accuracy. This advanced algorithm enhances road maintenance and safety by overcoming challenges in complex backgrounds and low-resolution imagery.

Keywords:

GELU YOLOv5 deep learning road crack detection transformer

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Area of Science:

Computer Vision
Artificial Intelligence
Civil Engineering

Background:

Manual road defect detection is inefficient and inaccurate.
Deep learning offers potential but faces challenges with crack detection complexity.
Existing methods struggle with varied backgrounds, low resolution, and crack similarity.

Purpose of the Study:

To develop a novel, highly accurate road crack detection algorithm.
To enhance the performance of existing YOLOv5 models for road defect identification.
To improve the efficiency and reliability of road maintenance inspections.

Main Methods:

Proposed a novel Road Defect Detection YOLOv5 (RDD-YOLOv5) algorithm.
Integrated transformer structure and explicit vision center for enhanced feature capture.
Replaced Sigmoid-weighted linear activations with Gaussian Error Linear Units for improved fitting.
Utilized a UAV flight platform for experimental evaluation.

Main Results:

RDD-YOLOv5 achieved a mean average precision of 91.48%.
The proposed model outperformed the original YOLOv5 by 2.5%.
Demonstrated accurate road crack identification in complex traffic environments.

Conclusions:

RDD-YOLOv5 offers a significant advancement in automated road crack detection.
The model effectively addresses challenges posed by complex backgrounds and image quality.
This technology promises to enhance road maintenance strategies and public safety.