With the development of modern society, traffic volume continues to increase in most countries worldwide, leading to an increase in the rate of pavement damage Therefore, the real-time and highly accurate pavement damage detection and maintenance have become the current need. In this paper, an enhanced pavement damage detection method with CycleGAN and improved YOLOv5 algorithm is presented. We selected 7644 self-collected images of pavement damage samples as the initial dataset and augmented it by CycleGAN. Due to a substantial difference between the images generated by CycleGAN and real road images, we proposed a data enhancement method based on an improved Scharr filter, CycleGAN, and Laplacian pyramid. To improve the target recognition effect on a complex background and solve the problem that the spatial pyramid pooling-fast module in the YOLOv5 network cannot handle multiscale targets, we introduced the convolutional block attention module attention mechanism and proposed the atrous spatial pyramid pooling with squeeze-and-excitation structure. In addition, we optimized the loss function of YOLOv5 by replacing the CIoU with EIoU. The experimental results showed that our algorithm achieved a precision of 0.872, recall of 0.854, and mean average [email protected] of 0.882 in detecting three main types of pavement damage: cracks, potholes, and patching. On the GPU, its frames per second reached 68, meeting the requirements for real-time detection. Its overall performance even exceeded the current more advanced YOLOv7 and achieved good results in practical applications, providing a basis for decision-making in pavement damage detection and prevention.

翻译：随着现代社会的发展，全球大多数国家的交通流量持续增长，导致路面损伤率上升。因此，实时且高精度的路面损伤检测与维护已成为当前需求。本文提出了一种结合CycleGAN与改进YOLOv5算法的增强型路面损伤检测方法。我们选取了7644张自采集的路面损伤样本图像作为初始数据集，并通过CycleGAN进行数据增强。由于CycleGAN生成的图像与真实道路图像存在显著差异，我们提出了一种基于改进Scharr滤波器、CycleGAN和拉普拉斯金字塔的数据增强方法。为提升复杂背景下的目标识别效果，并解决YOLOv5网络中空间金字塔池化-快速模块无法处理多尺度目标的问题，我们引入了卷积块注意力模块注意力机制，并提出了带有挤压-激励结构的空洞空间金字塔池化模块。此外，我们通过将CIoU替换为EIoU来优化YOLOv5的损失函数。实验结果表明，在检测裂缝、坑洞和修补这三种主要路面损伤类型时，我们的算法实现了0.872的精确率、0.854的召回率以及0.882的平均精度均值@0.5。在GPU上，其每秒帧数达到68，满足了实时检测的要求。其整体性能甚至超过了当前更先进的YOLOv7，并在实际应用中取得了良好效果，为路面损伤检测与防治的决策提供了依据。