Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require ex...Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.展开更多
基金This work is jointly supported by the National Natural Science Foundation of China under grant 61703347the Chongqing Natural Science Foundation grant cstc2016jcyjA0428+2 种基金the Common Key Technology Innovation Special of Key Industries under grant no. cstc2017zdcy-zdyf0252 and cstc2017zdcy-zdyfX0055the Artificial Intelligence Technology Innovation Significant Theme Special Project under grant nos. cstc2017rgzn-zdyf0073 and cstc2017rgznzdyf0033the China University of Mining and Technology Teaching and Research Project (2018ZD03, 2018YB10).
文摘Precise real-time obstacle recognition is both vital to vehicle automation and extremely resource intensive. Current deep-learning based recognition techniques generally reach high recognition accuracy, but require extensive processing power. This study proposes a region of interest extraction method based on the maximum difference method and morphology, and a target recognition solution created with a deep convolutional neural network. In the proposed solution, the central processing unit and graphics processing unit work collaboratively. Compared with traditional deep learning solutions, the proposed solution decreases the complexity of algorithm, and improves both calculation efficiency and recognition accuracy. Overall it achieves a good balance between accuracy and computation.
