Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation. Issue 2 (10th April 2019) - Qi, Guanqiu; Wang, Huan; Haner, Matthew; Weng, Chenjie; Chen, Sixin; Zhu, Zhiqin
Abstract : 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.