Pothole and patch detection on asphalt pavement using deep convolutional neural network

Abstract

The main obstacles to the widespread use of the PMS are the high financial and time costs for carrying out on-site assessments and the difficulty of processing and analyzing the data to generate the diagnoses of the current condition of the pavement. With technological advancement, some techniques such as computer vision, image processing, and machine learning can automatically extract the information of the pavements' condition. The present study proposes the exclusive use of images from cameras attached to a vehicle, simple collection and reduced cost, and extraction of information on pavement defects using a CNN. The research developed object detection models with YOLO architecture to identify potholes and patches. It was analyzed the metrics impact of the image size (224x224, 320x320, 416x416 pixels) and number of iterations for Yolo version 3 and 4. As expected, the increasing image size resulted in improved metrics results and the expansion of the iterations led to an improvement in the IoU. The CNN that presented the best overall performance, combining all the metrics, was based on Yolov3, with an image size of 416x416 and 6000 iterations training, in which it obtained an F1-score of 79.00%, an average IoU of 64.59%, and mAP@0.50 of 73.85%.