摘要
手和工具的交互是区分车间人员作业行为的关键信息。为防止泵件装配工序错漏,达到实时监测的目的,提出基于空间特征融合的车间作业工具检测算法。首先,为了提高对目标的定位能力和检测精度,基于帧差法分割前景中的手部运动区域,获得具有运动空间特征的纹理图像,结合装配过程的RGB图像构成目标检测网络的双通道输入。设计空间感知模块实现双通道输入的空间特征融合,获得全局空间信息。利用特征增强模块融合全局空间信息和深层语义信息,加强显著位置的特征响应。然后,采用ESNet(enhance shuffleNet)重构主干网络,基于深度可分离卷积实现多尺度特征提取,提高检测速度。最后,针对图像背景中局部元素变化问题,采用CutOut数据增强方法,提高模型抗干扰能力。实验结果表明,本文所提算法有效降低了误检率,较传统YOLOv5s的mAP提高6.4%,能够快速准确检测车间人员作业时使用的工具。
The interaction of hands and tools is the key information to distinguish the behavior of workers.To prevent the errors and omissions of the process in the assembly of pumps and achieve the purpose of real-time monitoring,a workshop tool detection algorithm based on spatial feature fusion is proposed.First,in order to improve the localization ability and detection accuracy of the object,the hand motion region in the foreground is segmented based on the frame difference method to obtain a texture map with hand spatial information,which is combined with RGB images of the assembly process to form a dual channel inputs to the object detection network.The spatial perception module is designed to realize the spatial feature fusion of the dual channel inputs and obtain the global spatial information.The feature enhancement module is proposed to mix the global spatial information and deep semantic information to enhance the feature response at salient locations.Then,the ESNet(enhance shuffleNet)is used to reconstruct the backbone network and form a multi-scale feature extraction module by deep separable convolution to improve the detection speed.Finally,in view of the local elements change both in the foreground and the background,the CutOut data enhancement method is used to improve the anti-interference capability.The experimental results show that the proposed algorithm can effectively reduce the false detection rate,and improve the mAP by 6.4%compared with the traditional YOLOv5s.The method can quickly and accurately detect the tools used by shop workers.
作者
王呈
黄义超
杨桂锋
Wang Cheng;Huang Yichao;Yang Guifeng(School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China;Wuxi Weifu High-Technology Group Cooperation,Wuxi 214031,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第3期39-49,共11页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金面上项目(61973137)
近地面探测技术重点实验室预研基金(6142414220203)项目资助。
关键词
工具检测
帧差法
双通道
注意力
tool detection
frame difference method
dual channel
attention
