文献类型：期刊文献

中文题名：基于加速区域卷积神经网络的夜间行人检测研究

英文题名：Nighttime Pedestrian Detection Based on Faster Region Convolution Neural Network

作者：叶国林[1,2];孙韶媛[1,2];高凯珺[1,2];赵海涛[3]

机构：[1]东华大学信息科学与技术学院,上海201620;[2]东华大学数字化纺织服装技术教育部工程研究中心,上海201620;[3]华东理工大学信息科学与工程学院,上海200237

年份：2017

卷号：54

期号：8

起止页码：117

中文期刊名：激光与光电子学进展

外文期刊名：Laser & Optoelectronics Progress

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金(61375007);上海市科委基础研究项目(15JC1400600)

语种：中文

中文关键词：图像处理;红外图像;行人检测;加速区域卷积神经网络;区域建议网络

外文关键词：image processing; infrared image; pedestrian detection; faster regional convolution neural network; region proposal network

摘要：行人检测是机器人和无人车夜间工作应用中的重要任务之一,采用加速区域卷积神经网络框架实现夜间红外图像中的行人检测,用区域建议网络生成候选区域,无需单独从图像中生成候选区域。区域建议网络和用于分类以及位置精修的卷积网络中,采用卷积层参数共享机制,使得该框架具有端到端的优点,因此无需手动选取目标特征,实现了从输入图像直接到行人检测的功能。实验结果表明,与使用传统方法和快速区域卷积神经网络相比,使用加速区域卷积网络框架对红外图像进行行人检测的准确率从68.2%和73.4%提高到了90.9%,检测时间从3.6s/frame和2.3s/frame缩短到了0.04s/frame,达到了实际应用中的实时性要求。
Pedestrian detection is one of the most important tasks of robots and unmanned vehicles at nighttime. Faster region convolution neural network framework is used to realize the pedestrian detection of infrared image at nighttime. This framework uses region proposal network to generate region proposals. Therefore, it is unnecessary to generate region proposals separately from the image. The parameter sharing mechanism is adopted in the convolutional layers in region proposal network and convolutional network for classification and bounding box regression, which makes the framework an end-to-end advantage. Thus, the pedestrian detection can be implemented from the input image to the detection result directly and it is unnecessary to manually select the features of the target. Experimental results show that the proposed method increases the recognition accuracy from 68.2% and 73.4% to 90.9% and shortens the recognition time from 3.6 s/frame and 2.3 s/frame to 0.04 s/frame compared with the traditional method and fast region convolution neural network, respectively, which reaches the required real-time level in practical applications.