Pavement Distress Detection Using YOLO and Faster RCNN on Edge Devices

In this study, transfer learning techniques will be used for model training, using edge computing [1] and deep learning object detection technology, combined with image road pothole detection applications, and deploying devices and tools that accelerate neural network operations, including DeepStream [2] and Intel NCS2. The performance and accuracy of model recognition will be compared, and finally, real-time streaming video technology will be used to present the results on the web. According to the experimental results, the best model achieved an mAP of 70.% in YOLOv4-tiny-3l, and in terms of operating efficiency, deployment on Jetson Xavier NX using DeepStream for acceleration can achieve 30FPS. Finally, the deep learning model recognizes the screen presented on the web. This application can improve the accuracy of Pavement Distress identification and help road maintenance units improve the efficiency of repairing roads.