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Mathematical Biosciences and Engineering, 2019, 16(6): 7982-7994. doi: 10.3934/mbe.2019402
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Embedded system for road damage detection by deep convolutional neural network
1 Department of Electronic and Computer Engineering, Southeast University Chengxian College, Nanjing 210088, China;
2 Department of Mechanical and Electrical Engineering, Southeast University Chengxian College, Nanjing 210088, China;
3 Verimake Research, Nanjing Qujike Info-tech Co., Ltd., Nanjing 210088, China
Received: , Accepted: , Published:
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.
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© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)
