Intelligent road crack detection and analysis based on improved YOLOv8
This provides an automated solution for road maintenance and safety monitoring, addressing inefficiencies and costs of manual inspection, though it is incremental as it builds on existing YOLOv8 methods.
The paper tackles road crack detection by developing an improved YOLOv8-based system that trains on 4029 images to recognize and segment cracks, achieving enhanced accuracy and efficiency through ECA and CBAM attention mechanisms.
As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring.