Automatic detection of pavement defects is of great importance for road maintenance and road condition rating assessment.To this end,a pavement defect detection system was designed using YOLOv5x combined with perspective transformation and image segmentation.First,a multi-type pavement defect dataset (PDD) was collected and produced to demonstrate the feasibility of the system.Then,four models single shot multibox detector (SSD),Faster R-CNN,you only look once v5x (YOLOv5x) and YOLOX,were used to train the PDDs for detection.After training,the mean average precision (mAP) of all four models exceeded 77%,with YOLOv5x showing the best results with 91% mAP,while proving the validity of the created dataset PDDs.Finally,YOLOv5x was used as the main detection method of the system combined with perspective transformation,image segmentation and skeleton extraction to obtain information such as length,width and area of defects,and then calculating the pavement condition index (PCI) to obtain the pavement damage level and the corresponding repair suggestions,improving the practicality of pavement defect detection.