Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation
Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation
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DOI：
10.1049/trit.2018.1045
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发表时间：
2019-06-01
影响因子：
5.1
通讯作者：
Zhu, Zhiqin
中科院分区：
文献类型：
--
作者：
Qi, Guanqiu;Wang, Huan;Zhu, Zhiqin
精确的实时障碍物识别对于车辆自动化至关重要，而且资源极其密集。目前基于深度学习的识别技术通常达到很高的识别精度，但需要大量的处理能力。本研究提出一种基于最大差分法和形态学的感兴趣区域提取方法，以及一种使用深度卷积神经网络创建的目标识别解决方案。在所提出的解决方案中，中央处理单元和图形处理单元协同工作。与传统的深度学习方法相比，该方法降低了算法复杂度，提高了计算效率和识别准确率。总体而言，它在准确性和计算性之间实现了良好的平衡。
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.