基于ResNet的安全监控目标检测  

Object Detection of Security Monitoring Based on ResNet

作　　者：孙毅 吴斯曼 方伟 吴双卿 胡超 SUN Yi;WU Siman;FANG Wei;WU Shuangqing;HU Chao(School of Information Science and Engineering,NingboTech University,Ningbo 315100,China)

机构地区：[1]浙大宁波理工学院信息科学与工程学院,宁波315100

出　　处：《集成技术》2024年第6期44-52,共9页Journal of Integration Technology

基　　金：浙江省自然科学基金项目(LQ17F030002);浙江省大学生科技创新活动计划项目(2022R438a007)。

摘　　要：《中华人民共和国道路交通安全法》要求摩托车驾驶人及乘坐人员应按规定戴安全头盔,因此,头盔佩戴智能视觉检测技术的需求应运而生。本文算法模型以交通监控视频图像中骑行人员佩戴头盔情况为研究对象,以YOLO目标检测框架为基础,首先采用分支吸收模块改善残差骨干网络,然后通过结构通道重组提升卷积层特征融合,最后应用设计的结构融合剪枝进一步压缩模型超参数。实验结果表明,该算法的精度和实时性较优,小目标检测效果也较好,多分类平均精度为88.8%,检测速度可达29.5帧/s,基本满足交通视频监控的需求。The“Road Traffic Safety Law of the People’s Republic of China”requires that motorcycle riders and passengers must wear safety helmets as stipulated by law.Consequently,the demand for intelligent visual detection technology for helmet wearing has emerged.This paper focuses on the study of helmet wearing by riders in traffic surveillance video images,based on the YOLO object detection framework.Initially,a branch absorption module is employed to improve the residual backbone network.Subsequently,the convolutional layer feature fusion is enhanced through structural channel recombination.Finally,a designed structural fusion pruning technique is applied to further compress the model’s hyperparameters.Experimental results indicate that the algorithm boasts superior accuracy and real-time performance,with effective detection of small targets.The average precision for multi-classification reaches 88.8%,and the detection speed can achieve up to 29.5 frames per second,which essentially meets the requirements of traffic video surveillance.

关 键 词：目标检测 特征融合 残差骨干网络 通道重组 结构融合剪枝 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]