摘要
地铁场景行人目标存在大小不一、不同程度遮挡以及环境过暗导致目标模糊等问题,很大程度影响了行人目标检测的准确性.针对上述问题,本研究提出了一种改进YOLOv5s目标检测算法以增强地铁场景行人目标检测的效果.构建地铁场景行人数据集,标注对应标签,进行数据预处理操作.本研究在特征提取模块中加入深度残差收缩网络,将残差网络、注意力机制和软阈值化函数相结合以增强有用特征信道,削弱冗余特征信道;利用改进空洞空间金字塔池化模块,在不丢失图像信息的前提下获得多尺度、多感受野的融合特征,有效捕获图像全局上下文信息;设计了一种改进非极大值抑制算法,对目标预测框进行后处理,保留检测目标最优预测框.实验结果表明:提出的改进YOLOv5s算法能有效提高地铁场景行人目标检测的精度,尤其对小行人目标和密集行人目标的检测,效果提升更为显著.
Pedestrian targets in subway scenes pose problems such as varying sizes,different degrees of occlusion,and blurred images caused by dark environments,which adversely affect the accuracy of pedestrian target detection.To address these problems,this study proposes an improved YOLOv5s target detection algorithm to improve the accuracy of pedestrian target detection in subway scene video signals.The pedestrian dataset of a subway scene is constructed,the corresponding labels are marked,and the data preprocessing operation is performed.Moreover,a deep residual shrinkage network is added to the feature extraction module,and the residual network,attention mechanism,and soft thresholding function are combined to enhance the useful feature channel and weaken the redundant feature channel.The fusion features of multiscale and multireceptive fields of the image are obtained using the improved atrous spatial pyramid pooling module without losing image information,and the global context information of the image is effectively captured.The improved nonmaximum suppression algorithm is designed to postprocess the target prediction frame and retain the optimal prediction frame of the detection target.The experimental results demonstrate that the improved YOLOv5s algorithm proposed in this study can effectively improve the accuracy of pedestrian target detection in subway scene video signals,particularly for small and dense pedestrian target scenes.
作者
张秀再
邱野
张晨
Zhang Xiuzai;Qiu Ye;Zhang Chen(School of Electronic and Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Province Atmospheric Environment and Equipment Technology Collaborative Innovation Center,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第6期134-143,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金青年科学基金(11504176,61601230)
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(BK20141004)。
