用改进YOLOv8实现道路裂缝智能检测与宽度分析
Intelligent road crack detection and analysis based on improved YOLOv8
- 基于YOLOv8融合ECA与CBAM注意力机制
- 在4029张图像上实现裂缝精准定位与宽窄测量
- 适合城市道路养护与智能交通系统应用
随着城市化加速和车流量增加，路面病害问题日益突出，严重威胁道路安全与使用寿命。传统坑槽检测依赖人工巡查，效率低且成本高。本文提出一种基于改进YOLOv8深度学习框架的智能道路裂缝检测与分析系统。通过训练4029张图像，构建目标分割模型，可高效准确识别并分割道路裂缝区域，并进一步分析分割结果，精确计算裂缝的最大最小宽度及其具体位置。实验表明，引入ECA与CBAM注意力机制显著提升模型检测精度与效率，为道路维护与安全监测提供新方案。
原文摘要 · Abstract (English)
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.
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