Multitarget detection of assembly parts based on improved YOLOv7

Jinhao Wang; Jizhuang Hui; Yaqian Zhang; Tao Zhou; Kai Ding

doi:10.1117/12.3014468

9 January 2024 Multitarget detection of assembly parts based on improved YOLOv7

Jinhao Wang, Jizhuang Hui, Yaqian Zhang, Tao Zhou, Kai Ding

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296927 (2024) https://doi.org/10.1117/12.3014468
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinhao Wang, Jizhuang Hui, Yaqian Zhang, Tao Zhou, and Kai Ding "Multitarget detection of assembly parts based on improved YOLOv7", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296927 (9 January 2024); https://doi.org/10.1117/12.3014468

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KEYWORDS

Object detection

Target detection

Detection and tracking algorithms

Education and training

RGB color model

Data modeling

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