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 precision@0.5 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.

本文提出了一个增强的道路损伤检测方法，利用 CycleGAN 和改进的 YOLOv5 算法，通过数据增强方法、注意力机制和空洞空间金字塔池化等技术，成功实现了道路损伤的实时、准确检测，并在实际应用中取得了良好结果。