Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8

Authors

Shuheng Wu, School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China; State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments, Xidian University, Xi'an 710071, China
Yongkui Liu, School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China; State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments, Xidian University, Xi'an 710071, China
Lin Zhang, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Yingying Xiao, Beijing Simulation Center, Beijing 100854, China; Beijing Institute of Electronic System Engineering, Beijing 100854, China
Lihui Wang, KTH Royal Institute of Technology, Stockholm 25175, Sweden

Abstract

Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck part, which significantly reduced the model's computational load and increased the detection speed. By combining the iRMB module with the EMA attention mechanism, an iEMA mechanism was proposed. Experimental results have indicated that on the self-made assembly workpiece dataset, the improved algorithm increases the F1 score by 3.2%, the mAP value by 1.7%, and the FPS by 11%, while reducing the model's parameter count by 47% and the computational complexity by 32% compared with the original algorithm.

Recommended Citation

Wu, Shuheng; Liu, Yongkui; Zhang, Lin; Xiao, Yingying; and Wang, Lihui (2025) "Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8," Journal of System Simulation: Vol. 37: Iss. 12, Article 11.
DOI: 10.16182/j.issn1004731x.joss.24-0910
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss12/11

First Page

3099

Last Page

3111

CLC

TP391.41

Recommended Citation

Wu Shuheng, Liu Yongkui, Zhang Lin, et al. Lightweight Assembly Workpiece Detection Algorithm Based on Improved YOLOv8[J]. Journal of System Simulation, 2025, 37(12): 3099-3111.

Corresponding Author

Liu Yongkui

DOI

10.16182/j.issn1004731x.joss.24-0910