摘要： 基于深度学习的车辆检测方法准确率较高，其在性能卓越的计算机与图形处理器设备上实时性较好，但在性能相对较低的嵌入式设备上实时性较差。在改进Tiny-YOLO网络的基础上，提出一种利用NCS2神经计算棒的嵌入式车辆检测方法。采用深度可分离卷积替换Tiny-YOLO网络标准卷积降低计算量，去除池化层并使用全卷积层以保留低级特征信息，采用Tensorflow深度学习框架训练改进的Tiny-YOLO网络，并将其部署到配备NCS2神经计算棒的嵌入式设备上。实验结果表明，与原始Tiny-YOLO网络相比，改进Tiny-YOLO网络检测实时性提高1倍，在MS COCO和VOC2007数据集上平均检测准确率分别提升1.12和0.23个百分点，配备NCS2神经计算棒后该方法检测的每秒传输帧数达到12，实时性较原始Tiny-YOLO网络大幅提高。

关键词: 车辆检测, 深度可分离卷积, 深度学习, Tiny-YOLO网络, 嵌入式设备

Abstract: The vehicle detection methods based on deep learning have high accuracy and excellent real-time performance for high-end computers and graphics processors, but their real-time performance is reduced for relatively low-end embedded devices. Based on the improved Tiny-YOLO network, this paper proposes a vehicle detection method using NCS2 neural computing stick for embedded devices. The Depthwise Separable Convolution (DSC) is used to replace the standard convolution of Tiny-YOLO network to reduce the amount of computation. The pooling layer is removed and the full convolution layer is used to retain the low-level feature information. The Tensorflow deep learning framework is used to train the improved Tiny-YOLO network and deploy it to the embedded device with the NCS2 neural computing stick. Experimental results show that compared with the original Tiny-YOLO network, the improved Tiny-YOLO network doubles the real-time performance, and increases the average detection accuracy by 1.12 and 0.23 percentage points respectively on MS COCO and VOC2007 datasets. After equipped with NCS2 neural computing stick, the number of frames per second detected by the proposed method reaches 12, which greatly improves the real-time performance compared with the original Tiny-YOLO network.

Key words: vehicle detection, Depthwise Separable Convolution (DSC), deep learning, Tiny-YOLO network, embedded device

中图分类号: 

• TP391.41