Multitarget detection of assembly parts based on improved YOLOv7

Abstract

Aiming at multi-target detection in complex human-robot collaborative assembly scenes, an improved YOLOv7 algorithm is proposed. Specifically, the Wise-Intersection over Union(Wise-IoU) loss function and the BiFormer attention module are introduced to improve the recognition performance of small assembly parts. Taking a worm-gear decelerator as an example, a dataset for assembly parts recognition is made. By training the improved network in the self-made dataset, the mAP@.5 value is increased by 3.25 % and the average total loss is reduced by 0.02365. The experiment results show that the improved YOLOv7 algorithm can achieve multi-assembly parts detection in collaborative assembly.