针对国内水泥路面病害检测数据集缺乏、规模小、场景单一，以及深度学习算法在复杂场景下泛化能力不足的问题，提出一种基于改进YOLOv5的路面病害检测算法。收集并构建包含11 862张图像的水泥路面病害检测数据集，覆盖9类场景下的3类最常见病害类型；通过融合以IoU度量的 K-Means 聚类算法和遗传算法获取模型训练的先验锚框；在特征增强阶段，引入轻量级上采样模块（CARAFE），减少特征重组过程中的信息损失；引入顾及通道、高度和宽度维度的多维协同注意力模块（MCA），增强多尺度病害特征的辨别力。实验结果表明：所提算法在保持较快推理速度的前提下，F ₁分数和平均精确率（mAP）分别达到75.5%和81.6%，优于5种主流的目标检测算法。实例分析表明：基于改进YOLOv5的路面病害检测算法能够满足大规模水泥路面病害智能检测与破损状况评价的实际需求。

Aiming at the problems of lack of domestic cement pavement disease detection data sets, small scale, single scene, and insufficient generalization ability of deep learning algorithms in complex scenes, a pavement disease detection algorithm based on improved YOLOv5 is proposed. A cement pavement disease detection dataset containing 11 862 images was collected and constructed, covering 3 most common disease types in 9 scenarios. The prior anchor frame of model training is obtained by combining the K-Means clustering algorithm measured by IoU and genetic algorithm. In the feature enhancement stage, a lightweight upsampling module (CARAFE) is introduced to reduce the information loss in the feature recombination process. A multi-dimensional collaborative attention module (MCA) considering channel, height and width dimensions is introduced to enhance the discrimination of multi-scale disease features. The experimental results show that under the premise of maintaining fast inference speed, the average of F ₁ score and average precision (mAP) of the proposed algorithm reach 75.5% and 81.6%, respectively, which are better than the five mainstream target detection algorithms. The example analysis shows that the pavement disease detection algorithm based on improved YOLOv5 can meet the actual needs of large-scale cement pavement disease intelligent detection and damage condition evaluation.