Equipment Identification and Localization Method Based on Improved YOLOv5s Model for Production Line
Open accessAbstract
Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation.
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An upgraded YOLOv5s model for real-time identification and localization of production line equipment, including robotic arms and AGV carts, achieved precision of 93.6%, recall of 85.6%, and mAP of 91.8%.
“An upgraded YOLOv5s model used for real-time identification and localization of production line equipment, including robotic arms and AGV carts. Achieved precision of 93.6%, recall of 85.6%, and mAP of 91.8%.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [122]
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- Authors
- Yu, M., Wan, Q., Tian, S., Hou, Y., Wang, Y., & Zhao, J.
- Affiliations
- Tianjin Chengjian University (CN)
- Corresponding author
- Zhao, Jian
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-12-19
- Volume
- 22
- Issue
- 24
- Pages
- 10011
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9785116
- OpenAlex
- W4312194675
- Cited by
- 10
- References
- 45
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 3
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation of Tianjin (16JCZDJC38600, 20YDTPJC00610)
- Tianjin Enterprise Science and Technology Commissioner Project (16JCZDJC38600, 20YDTPJC00610)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- equipment identification, object localization, production line monitoring, YOLOv5, GSConv, Slim-Neck, decoupled head, robot manipulators, intelligent video surveillance, industrial digitalization
- MeSH terms
- Artificial Intelligence, Head, Image Processing, Computer-Assisted, Industry, Intelligence
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