In order to achieve intelligent rapid detection of pavement defects, the deep learning object detection algorithm YOLOv5 is improved, and the three detection models （YOLOv5A, YOLOv5C, YOLOv5AC）can be quickly detected by video detection. Using smart phones and digital cameras to collect road defect images and make data sets, to meet the needs of video detection, the use of Kmeans algorithm and 1IoU as sample distance recluster anchor, to obtain better anchor frame parameters; the introduction of CBAM attention mechanism in multiple structures of the network, enhance the feature extraction ability of the model. The experimental results show that the average accuracy of the YOLOv5C algorithm on the training set reaches 918%, which is 1% higher than that of the original model. The average accuracy of the YOLOv5A algorithm on the verification set reaches 927%, which is 17% higher than that of the original model. In terms of actual detection effect, the YOLOv5AC algorithm achieves 89%, 62%, and 90% in the identification accuracy of cracks, broken plates and pits, which is 45%, 4%, and 5% higher than the original model. And the detection speed of the model reaches 40 FPS. YOLOv5AC algorithm has high detection accuracy and recognition speed, and can meet the intelligent realtime detection requirements in road defect detection under certain conditions.