针对目前沥青路面裂缝目标检测技术在面对复杂路面情况（强光、积水、杂物等干扰因素）时识别精度较低的问题，通过构建沥青路面裂缝数据集，提出了一种基于改进YOLOv5s的沥青路面裂缝检测算法。首先，根据沥青路面裂缝数据集特点，对YOLOv5s原模型进行了以下改进：使用K-means + +算法重新聚类裂缝数据集锚框，获得适合沥青路面裂缝特点的初始锚框；在模型的Prediction部分添加CBAM（convolutional block attention module）注意力模块，提高模型对于细小裂缝的检测能力；采用CIoU_Loss函数作为模型回归损失函数，提高锚框定位精度。其次，对YOLOv5s改进模型进行消融实验，证明了各项改进方案均能够提升模型检测能力，且互不冲突。最后，将YOLOv5s改进模型与各类经典目标检测模型分别在本文数据集、CFD数据集、Crack500数据集以及Crack200数据集上进行对比实验，实验结果表明，YOLOv5s改进模型在各个数据集上的检测效果均优于其他目标检测模型，并且该模型在本文数据集上的mAP@0.5以及mAP@［0.5:0.95］分别为90.58%、56.08%，远高于其他目标检测模型，表明YOLOv5s改进模型在面对复杂路面情况时检测效果较好，可为沥青路面裂缝自动化检测提供理论依据。

In view of the low identification accuracy of the crack detection technology of asphalt pavement under the current conditions of complex pavement, an asphalt pavement crack detection algorithm based on improved YOLOv5s was proposed by building the dataset for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack dataset: obtain the initial anchor box matched with the characteristics of the asphalt pavement cracks by re-clustering the anchor box of the crack dataset using the K-means + + algorithm; add the convolutional block attention module(CBAM)to the Prediction part of the model to improve the model's ability to detect small cracks; take the CIoU_Loss function as the model regression loss function to improve the anchor box location accuracy. The second step was to perform an ablation experiment on the improved YOLOv5s model, Which would prove that each improvement scheme could improve detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the dataset of this paper: the CFD dataset, the Crack500 dataset and the Crack200 dataset. The results show that the detection of the improved YOLOv5s model on each dataset was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the dataset were 90.58% and 56.08%, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks.