Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)

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An Improved YOLOv8 Algorithm Model for Detection of Personal Protective Products in Chemical Plants

Authors
Hekang Cheng1, Suqun Cao1, *, Daheng Wang1, Xinze Shen1, Kun Zhao2, Jianhui Wu1, Tao Jiang1, Haitao Zhou1, Di Zhang1, Jianxue Zhao1
1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS, 223003, China
2School of Art and Design, Guilin University of Electronic Science and Technology, Guilin, JS, 541004, China
*Corresponding author. Email: caosuqun@126.com
Corresponding Author
Suqun Cao
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-490-7_53How to use a DOI?
Keywords
YOLOv8; GUA; SCA; Chemical plant protective products
Abstract

With the rapid development of society and the acceleration of industrialization, the chemical industry, with its irreplaceable position, has become an important pillar of the national economy. In the key field of chemical plants, the issue of production safety is particularly prominent. Operators are in such a special environment, whether the personal protection measures are in place is directly related to their personal safety, this paper is based on the YOLOv8 algorithmic model, respectively, in the YOLOv8 backbone network and the Neck part of the optimization, which is mainly to optimize the Bottleneck module in the backbone network to the MishNextBlock module, and in the YOLOv8 Neck's pyramid structure, two modules are added: Spatial and Channel Attention (SCA) and Global Attention Upsample (GAU), which are fused into a new Path Aggregation Network (PAN) structure. PAN structure, thus forming the improved YOLOv8 algorithm model. And it is shown experimentally that the improved YOLOv8 algorithm realizes a significant improvement in the detection accuracy of small-scale targets, near and far distance multi-scale and fuzzy small targets in chemical plant protective equipment.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 August 2024
ISBN
978-94-6463-490-7
ISSN
2589-4919
DOI
10.2991/978-94-6463-490-7_53How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Hekang Cheng
AU  - Suqun Cao
AU  - Daheng Wang
AU  - Xinze Shen
AU  - Kun Zhao
AU  - Jianhui Wu
AU  - Tao Jiang
AU  - Haitao Zhou
AU  - Di Zhang
AU  - Jianxue Zhao
PY  - 2024
DA  - 2024/08/31
TI  - An Improved YOLOv8 Algorithm Model for Detection of Personal Protective Products in Chemical Plants
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 487
EP  - 496
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_53
DO  - 10.2991/978-94-6463-490-7_53
ID  - Cheng2024
ER  -