Article
Version 1
Preserved in Portico This version is not peer-reviewed
A Glove-wearing Detection Algorithm Based on Improved YOLOv8
Version 1
: Received: 1 November 2023 / Approved: 2 November 2023 / Online: 2 November 2023 (04:07:54 CET)
A peer-reviewed article of this Preprint also exists.
Li, S.; Huang, H.; Meng, X.; Wang, M.; Li, Y.; Xie, L. A Glove-Wearing Detection Algorithm Based on Improved YOLOv8. Sensors 2023, 23, 9906. Li, S.; Huang, H.; Meng, X.; Wang, M.; Li, Y.; Xie, L. A Glove-Wearing Detection Algorithm Based on Improved YOLOv8. Sensors 2023, 23, 9906.
Abstract
Wearing gloves while operating machinery in workshops is an essential precaution to prevent mechanical injuries and burns from high temperatures, among other potential hazards. Ensuring workers are properly equipped with gloves, which is a crucial measure in accident prevention. Glove images often occupy a minimal proportion of the frame and are easily obscured by cluttered backgrounds, especially with limited edge computing resources. Consequently, this study proposes a glove detection algorithm called YOLOv8-AFPN-M-C2f based on YOLOv8, offering swifter detection speeds, lower computational demands, and enhanced accuracy for workshop scenarios. This research innovates by substituting the head of YOLOv8 with the AFPN-M-C2f network, amplifying the pathways for feature vector propagation, and mitigating semantic discrepancies between non-adjacent feature layers. Additionally, the introduction of a superficial feature layer enriches surface feature information, augmenting the model's sensitivity to smaller objects. To assess the performance of the YOLOv8-AFPN-M-C2f model, we conducted multiple experiments using a factory glove detection dataset compiled for this study. The results indicate that the enhanced YOLOv8 model surpasses other network models. Compared to the baseline YOLOv8 model, the refined version shows a 2.6% increase in mAP@50%, a 90.1% rise in FPS, and a 13% reduction in the number of parameters. This research contributes an effective solution for the detection of glove adherence.
Keywords
Glove-wearing detection; YOLOv8; Feature Pyramid Network; Feature Layer
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment