ArticleSensors (Basel, Switzerland)2022
Equipment Identification and Localization Method Based on Improved YOLOv5s Model for Production Line.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Enhancements and On-Site Experimental Study on Fall Detection Algorithm for Students in Campus Staircase.Sensors (Basel, Switzerland) · 2025Article
- A Lightweight Model for Real-Time Detection of Vehicle Black Smoke.Sensors (Basel, Switzerland) · 2023Article
- Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
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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Registered trials
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