Embedded system for road damage detection by deep convolutional neural network

Road pavement could be damaged due to various reasons, causing damages such as cracks and pits. These damages cause potential dangers in traffic safety. It is necessary for road maintenance departments to find damages in time before maintenance. At present, maintenance departments of some high-level roads are equipped with specialized detection vehicles such as laser scanning vehicles to detect road damages. These kinds of devices can get good detection performance, but the economic cost is very high. In this paper, we use a road damage image dataset to train an object detection model based on deep convolutional neural network and deploy it on a low-cost embedded platform to form an embedded system. The system uses a common camera mounted on windshield of a common vehicle as sensor to detect road damages. The embedded system consumes about 352 ms to process one frame of image and can achieve a recall rate of about 76% which is higher than some previous related works. The recall rate of this scheme using common cameras is less than that of high-level specialized detectors, but the economic cost is much lower than them. After subsequent development, the road maintenance department with limited funds can consider about schemes like this.