An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8
High-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/5/850 |
| _version_ | 1797264660382089216 |
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| author | Tingyao Jiang Zhao Li Jian Zhao Chaoguang An Hao Tan Chunliang Wang |
| author_facet | Tingyao Jiang Zhao Li Jian Zhao Chaoguang An Hao Tan Chunliang Wang |
| author_sort | Tingyao Jiang |
| collection | DOAJ |
| description | High-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm based on the YOLOv8 model to address these challenges. Our paper introduces several improvements to enhance its performance in detecting safety belts. First, to enhance the feature extraction capability, we introduce a BiFormer attention mechanism. Moreover, we used a lightweight upsampling operator instead of the original upsampling layer to better preserve and recover detailed information without adding an excessive computational burden. Meanwhile, Slim-neck was introduced into the neck layer. Additionally, extra auxiliary training heads were incorporated into the head layer to enhance the detection capability. Lastly, to optimize the prediction of bounding box position and size, we replaced the original loss function with MPDIOU. We evaluated our algorithm using a dataset collected from high-altitude work scenarios and demonstrated its effectiveness in detecting safety belts with high accuracy. Compared to the original YOLOv8 model, the improved model achieves P (precision), R (recall), and mAP (mean average precision) values of 98%, 91.4%, and 97.3%, respectively. These values represent an improvement of 5.1%, 0.5%, and 1.2%, respectively, compared to the original model. The proposed algorithm has the potential to improve workplace safety and reduce the risk of accidents in high-altitude work environments. |
| first_indexed | 2024-04-25T00:32:26Z |
| format | Article |
| id | doaj.art-a35624a0321042f0adf1df79f7daa679 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-25T00:32:26Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a35624a0321042f0adf1df79f7daa6792024-03-12T16:42:22ZengMDPI AGElectronics2079-92922024-02-0113585010.3390/electronics13050850An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8Tingyao Jiang0Zhao Li1Jian Zhao2Chaoguang An3Hao Tan4Chunliang Wang5College of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaCollege of Computer and Information, China Three Gorges University, Yichang 443002, ChinaHigh-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm based on the YOLOv8 model to address these challenges. Our paper introduces several improvements to enhance its performance in detecting safety belts. First, to enhance the feature extraction capability, we introduce a BiFormer attention mechanism. Moreover, we used a lightweight upsampling operator instead of the original upsampling layer to better preserve and recover detailed information without adding an excessive computational burden. Meanwhile, Slim-neck was introduced into the neck layer. Additionally, extra auxiliary training heads were incorporated into the head layer to enhance the detection capability. Lastly, to optimize the prediction of bounding box position and size, we replaced the original loss function with MPDIOU. We evaluated our algorithm using a dataset collected from high-altitude work scenarios and demonstrated its effectiveness in detecting safety belts with high accuracy. Compared to the original YOLOv8 model, the improved model achieves P (precision), R (recall), and mAP (mean average precision) values of 98%, 91.4%, and 97.3%, respectively. These values represent an improvement of 5.1%, 0.5%, and 1.2%, respectively, compared to the original model. The proposed algorithm has the potential to improve workplace safety and reduce the risk of accidents in high-altitude work environments.https://www.mdpi.com/2079-9292/13/5/850high-altitude worksafety belt detectionyolov8 |
| spellingShingle | Tingyao Jiang Zhao Li Jian Zhao Chaoguang An Hao Tan Chunliang Wang An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 Electronics high-altitude work safety belt detection yolov8 |
| title | An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 |
| title_full | An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 |
| title_fullStr | An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 |
| title_full_unstemmed | An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 |
| title_short | An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8 |
| title_sort | improved safety belt detection algorithm for high altitude work based on yolov8 |
| topic | high-altitude work safety belt detection yolov8 |
| url | https://www.mdpi.com/2079-9292/13/5/850 |
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