机械科学与技术2024，Vol.43Issue(8)：1418-1426,9.DOI:10.13433/j.cnki.1003-8728.20240088
人机协同装配多目标检测的改进YOLOv7算法
Improved YOLOv7 Algorithm for Multi-object Detection Method of Human-robot Collaboration Assembly
摘要
Abstract
Aiming at the characteristics of complex and changeable Human-Robot Collaborative assembly environment,large scale difference with the assembly parts,and high similarity of some parts,in order to ensure that the robot can grasp the assembly parts accurately in the Human-Robot Collaborative assembly,an improved YOLOv7 model is proposed to improve the multi-part target detection effect in the assembly scene.Firstly,ODConv(Omni-Dimensional Dynamic Convolution)is used to replace the convolutional layers in the YOLOv7 backbone network,so that it can adjust the weight of the convolutional kernel adaptively and extract the features of assembly parts of different shapes and sizes.Secondly,the SIAM(Selective Image Attention Mechanism)model was introduced into the YOLOv7 backbone network to reduce the influence of the complex and variable assembly environment backgrounds on the detection accuracy of parts.Finally,Efficient-IOU is used to replace the original Complete-IOU to accelerate convergence and reduce the influence of the high similarity of some assembly parts on the detection accuracy.Experimental results show that the average accuracy of the model is 93.4%,and the improved network is superior to the original network and other target detection algorithms.The present improved YOLOv7 algorithm has high FPS while maintaining high precision,relatively low model parameters,and computational load,and is suitable for the real-time target detection requirements in dynamic Human-Robot Collaborative assembly scenarios.关键词
人机协同装配/YOLOv7/注意力机制/E-IOU/装配零件检测/多目标检测Key words
human-robot collaborative assembly/YOLOv7/attention Mechanism/E-IOU/assembly parts detection/multi-object detection分类
信息技术与安全科学引用本文复制引用
惠记庄,王锦豪,周涛,张雅倩,丁凯..人机协同装配多目标检测的改进YOLOv7算法[J].机械科学与技术,2024,43(8):1418-1426,9.基金项目
中国博士后科学基金项目(2022T150073)与陕西省秦创原"科学家+工程师"团队建设项目(2022KXJ-150) （2022T150073）
