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.