Recognition of unsafe behaviors of key position personnel in coal mines based on improved YOLOv7 and ByteTrack
| Autor: | HAN Kang, LI Jingzhao, TAO Rongying |
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| Jazyk: | čínština |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 50, Iss 3, Pp 82-91 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x |
| DOI: | 10.13272/j.issn.1671-251x.2024030015 |
| Popis: | The application of artificial intelligence technology can real-time recognize the behavior of key position personnel in coal mines, such as mine hoist drivers, to prevent dangerous situations such as equipment misoperation. It is of great significance for ensuring coal mine safety production. The personnel behavior recognition method based on image features has problems of poor resistance to background interference and insufficient real-time performance. In order to solve the above problems, a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack is proposed. Firstly, based on MobileOne and C3, lightweight improvements are made to the backbone and head network of the YOLOv7 object detection model to improve the inference speed of the model. Secondly, integrating ByteTrack tracking algorithm, to achieve the tracking and locking of personnel is achieved, and the capability to resist background interference is improved. Thirdly, MobileNetV2 is used to optimize the network structure of OpenPose and improve the efficiency of skeleton feature extraction. Finally, the spatial temporal graph convolutional networks (ST−GCN) is used to analyze the spatial structure and dynamic changes of the key points of the human skeleton in the time series, achieving the analysis and recognition of unsafe behaviors. The experimental results show that the precision of the MobileOneC3−YOLO model reaches 93.7%, and the inference speed is improved by 52% compared to the YOLOv7 model. The success rate of personnel locking model integrating ByteTrack reaches 97.1%. The improved OpenPose model reduces memory requirements by 170.3 MiB. The inference speed on CPU and GPU is improved by 74.7% and 54.9%, respectively; The recognition precision of the unsafe behavior recognition model for four types of unsafe behaviors, including fatigue sleeping on duty, leaving work, side talking, and playing with mobile phones, reaches 93.5%, and the inference speed reaches 18.6 frames per second. |
| Databáze: | Directory of Open Access Journals |
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