Stacked Workpiece Detection Based on Improved YOLOv5 Algorithm
In the current complex assembly scenarios,various workpieces are stacked together,which brings challenges to the accurate recognition of assembly robots.To solve the problem,this paper proposes an im-proved YOLOv5 model for the detection and recognition of stacked workpieces.The EIFM edge informa-tion fusion module is used to enhance the contour information of the target samples;the MAM multi-scale attention module is added at the end of the feature extraction network to enhance the detection of complex scenes and smaller targets;the PANet path aggregation network in the original YOLOv5's Neck network is replaced with the BiFPN bi-directional feature pyramid fusion structure,which performs weighted feature fusion on high and low feature information;finally,the traditional non-great feature fusion network is re-placed with a BiFPN bi-directional feature pyramid fusion structure.fusion;finally,the traditional non-great suppression algorithm is changed to DIOU_NMS to reduce the leakage of detection due to mutual occlusion of artifacts.The algorithm comparison experiments and stacking degree comparison experiments show that:the mAP of the improved YOLOv5 algorithm reaches 97.8%,which is 7.25%higher than the pre-im-provement;the mAP of the target detection in the low,medium and high stacked workpiece datasets reaches 98.76%,97.93%and 94.96%,which is 0.67%,1.56%,4.41%higher than the pre-improvement YOLOv5 algorithm,respectively.Compared with the original YOLOv5 algorithm,the improved algorithm model achieves more accurate identification and localization of workpieces with high stacking degree.
万方数据
维普
