摘要
在当前工业的螺栓生产过程中,堆叠螺栓的分拣工作依然需要人工完成,不仅工作效率低,而且会导致大量人力资源的浪费。针对这一问题,对YOLOv5网络模型进行了改进,提出了SE_YOLOv5网络模型。首先,在原网络的Neck部分删除了P′1特征层,减小了网络对浅层信息的提取,在不影响对大尺寸目标检测的前提下,提高了网络检测的实时性;然后,改进了Backbone模块,通过添加压缩与激励(SE)注意力机制,使网络更高效地聚焦于图像中的重要部分,增强了网络对堆叠螺栓检测的准确性;最后,提出了检测框重叠最小法,减少了抓取时夹爪与非目标螺栓的碰撞,并对螺栓检测框进行了抓取点位姿优化,提高了抓取的成功率。研究结果表明:SE_YOLOv5网络对堆叠螺栓检测的平均精度为86.5%,平均速度为13.02 FPS;相比于原YOLOv5s网络模型,SE_YOLOv5网络在检测精度上提升了1.2%,在检测速度上提升了2.71 FPS;相比于其他检测模型,SE_YOLOv5也具有更高的检测精度和检测速度。抓取结果证明,该模型能用于有效地指导机械臂进行螺栓抓取操作。
In the current industrial bolt production process,the sorting of stacked bolts still needs to be completed manually,which not only has low work efficiency,but also leads to the waste of a large number of human resources.Aiming at this problem,the YOLOv5 network model was improved,and a SE_YOLOv5 network model was proposed.Firstly,the P′1 feature layer was deleted in the Neck part of the original network,which reduced the extraction of shallow information by the network,and improved the real-time performance of network detection without affecting the detection of large-size objects;Then,the Backbone module was improved to make the network focus on the important parts of the image more efficiently by adding the squeeze-and-excitation(SE)attention mechanism,and the accuracy of the network s detection of stacked bolts was enhanced;Finally,the minimum overlap method of the detection frame was proposed to reduce the collision between the gripper and the non-target bolt during grasping,and the grasping point pose of the bolt detection frame was optimized to improve the success rate of grasping.The research results show that the average accuracy of the SE_YOLOv5 network is 86.5%and the average detection speed is 13.02 FPS.Compared with the original YOLOv5s network model,the SE_YOLOv5 network has improved the detection accuracy by 1.2%and the detection speed by 2.71 FPS,and the SE_YOLOv5 also has higher detection accuracy and detection speed than other detection models.The grasping experiment shows that the model can effectively guide the robotic arm to carry out bolt grasping operation.
作者
李凤洋
邱益
陈江义
杨云峰
窦晓亮
郝树涛
LI Fengyang;QIU Yi;CHEN Jiangyi;YANG Yunfeng;DOU Xiaoliang;HAO Shutao(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China;Engineering Construction Branch,Qinghai Yellow River Upstream Hydropower Development Co.,Ltd.,Xining 810000,China)
出处
《机电工程》
CAS
北大核心
2024年第8期1500-1507,共8页
Journal of Mechanical & Electrical Engineering
基金
河南省科技研发计划联合基金(产业类)资助项目(225101610073)。
关键词
堆叠螺栓分拣
SE_YOLOv5网络模型
压缩与激励注意力机制
重叠最小法
抓取操作
抓取点位姿优化
sorting of stacked bolts
SE_YOLOv5 network model
squeeze-and-excitation(SE)attention mechanism
minimal overlap method
grasping operations
point-taking attitude optimization
