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11 July 2024 Design of enterprise worker safety detection algorithm based on YOLO
Xin Li, Norriza Binti Hussin, Nor Alina Binti Ismail
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Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101D (2024) https://doi.org/10.1117/12.3034764
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China
Abstract
Protecting the personal safety of on-site workers is an important task in enterprise production. In order to achieve widespread deployment to edge computing terminals, a lightweight object detection algorithm based on YOLOv5 is used to implement the personal safety detection task for workers. To achieve a lightweight task, PConv is utilized as the convolutional layer to decrease computational complexity, while Bi-Level Routing Attention is incorporated to enhance model accuracy. Furthermore, four detection heads are employed to improve object recognition capabilities. After experimentation, the precision can be improved by 3.4% compared with the baseline model, the parameters are reduced by 1.91MB, and the model size is decreased by 3.2MB.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xin Li, Norriza Binti Hussin, and Nor Alina Binti Ismail "Design of enterprise worker safety detection algorithm based on YOLO", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101D (11 July 2024); https://doi.org/10.1117/12.3034764
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KEYWORDS
Object detection
Detection and tracking algorithms
Safety
Target detection
Internet of things
Safety equipment
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