基于改进SSD算法的地铁场景小行人目标检测  

Small Pedestrian Target Detection in Subway Scene Based on Improved SSD Algorithm

作　　者：张秀再[1,2] 邱野 沈涛 Zhang Xiuzai;Qiu Ye;Shen Tao(School of Electronic and Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044;Jiangsu Provincial Atmospheric Environment and Equipment Technology Collaborative Innovation Center(Nanjing University of Information Science&Technology),Nanjing 210044)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]江苏省大气环境与装备技术协同创新中心(南京信息工程大学),南京210044

出　　处：《计算机研究与发展》2025年第2期397-407,共11页Journal of Computer Research and Development

基　　金：江苏省高校自然科学研究项目(13KJA510001);江苏省自然科学基金青年基金项目(BK20141004);国家自然科学基金青年科学基金项目(11504176,61601230)。

摘　　要：在地铁场景中,小行人目标由于分辨率低,包含特征信息较少,现阶段目标检测器对此类目标的检测仍具有挑战性.SSD目标检测算法利用金字塔网络的多尺度检测头,能一定程度提高行人目标检测性能,但将其应用于地铁等复杂环境中实现小行人目标检测仍具有一定局限性.针对上述问题,提出一种改进SSD算法以加强地铁场景中小行人目标检测效果.通过构建地铁场景行人目标数据集,标注相应标签,同时进行数据预处理操作;在特征提取网络中加入金字塔特征加强模块,将多分支残差单元、亚像素卷积和特征金字塔相结合获得图像多尺度、多感受野融合特征;利用上下文信息融合模块将图像低层特征与上下文特征相融合,生成扩展特征层用于检测小行人目标;设计一种基于Anchor-free的动态正负样本分配策略,为小行人目标生成最优正样本.实验结果表明:提出的改进SSD算法能有效提高地铁场景小行人目标检测性能,对遮挡严重的小行人目标检测,效果提升更为明显.In the subway scene,small pedestrian targets contain less feature information due to their low resolution,and it is still challenging for object detectors to detect such objects at this stage.SSD target detection algorithm uses the multi-scale detection head of the pyramid network,which can improve the pedestrian target detection performance to a certain extent,but it still has certain limitations in small pedestrian target detection application in complex environments such as subways.In view of the above problems,we propose an improved SSD algorithm to enhance the detection effect of small pedestrian targets in subway scenes,construct a dataset of pedestrian targets in subway scenes,mark the corresponding labels,and perform data preprocessing operations at the same time.In this study,a pyramid feature enhancement module is added to the feature extraction network,and the multi-branch residual unit,sub-pixel convolution and feature pyramid are combined to obtain image multi-scale and multi-receptive field fusion features.We use the context information fusion module to fuse the low-level features of the image with the context features to generate an extended feature layer for detecting small pedestrian targets,and design an Anchor-free dynamic positive and negative sample allocation strategy to generate optimal positive samples for small pedestrian targets.A dynamic positive and negative sample allocation strategy based on Anchor-free is designed to generate optimal positive samples for small pedestrian targets.The experimental results show that the proposed improved SSD algorithm can effectively improve the performance of small pedestrian target detection in subway scenes,and the effect of small pedestrian target detection with severe occlusion is more obvious.

关 键 词：小行人目标检测 SSD算法 注意力机制 亚像素卷积 多尺度特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]