基于深度可分离卷积和多级特征金字塔网络的行人检测

为提高行人检测的准确率,提出一种基于卷积神经网络的行人检测方法。该方法以YOLOv3-tiny算法为基础,在骨干网络部分,用深度可分离卷积的网络结构代替原卷积网络结构,加深网络深度。在检测部分,提出一种改进的多级特征金字塔网络,该网络由8个结构相同的使用深度可分离卷积的特征金字塔组成,特征金字塔之间串联连接,将不同金字塔得到的相同尺寸的特征进行融合,利用融合后的特征金字塔进行检测。在Caltech Pedestrian数据集上进行测试。结果表明:该方法的漏检率为57.83%,比梯度方向直方图(HOG)方法低32.53%,比基于深度学习的方法SA Fast-RCNN和MS-CNN分别低4.67%、3.21%;运行速度为34 ms/帧。因而,该方法满足了实时性要求。

Pedestrian detection based on depthwise separable convolution and multi-level feature pyramid network

A pedestrian detection method was proposed based on convolutional neural network to improve the accuracy of pedestrian detection.The method took a YOLOv3-tiny algorithm as a base.In the backbone network part,in order to deepen the network depth,an original convolutional network structure was replaced by a depthwise separable convolution.In the detection part,an improved multi-level feature pyramid network was proposed.The network consisted of eight feature pyramids with the same structure.The feature pyramid was also composed of depthwise separable convolutions.The feature pyramid was connected in series,features of the same size obtained by different pyramids were merged.Then the fused feature pyramid was used for detection.Tests on a Caltech Pedestrian dataset were done.The results show that the miss rate of this method is 57.83%,which is 32.53%lower than that of the histogram of oriented gradient(HOG)method,and 4.67%,3.21%lower than that of the deep learning method SA Fast-RCNN and MS-CNN,with a running speed of 34 ms/frame.Therefore,this method meets the real-time requirement.