Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal
Abstract Aiming at the problems of low detection accuracy, high computational complexity and long‐time consumption of visual perception model in a complex mining environment, this research designs a visual information perception system of coal mine comprehensive excavation working face for an edge c...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13206 |
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| author | Dongyang Zhao Guoyong Su Pengyu Wang |
| author_facet | Dongyang Zhao Guoyong Su Pengyu Wang |
| author_sort | Dongyang Zhao |
| collection | DOAJ |
| description | Abstract Aiming at the problems of low detection accuracy, high computational complexity and long‐time consumption of visual perception model in a complex mining environment, this research designs a visual information perception system of coal mine comprehensive excavation working face for an edge computing terminal. Firstly, the C3‐Fast feature extraction module, spatial pyramid pooling with cross‐stage partial connection (SPPCSPC) pooling module, bi‐directional feature pyramid network and lightweight decoupled detection head are used to optimize the YOLOv5s model, so as to construct the FSBD‐YOLOv5s multi‐object detection model. Secondly, the pruning and distillation algorithm is used to lighten the FSBD‐YOLOv5s model, and the model complexity is greatly reduced while maintaining the model detection accuracy. Further, the lightweight FSBD‐YOLOv5s model is migrated and deployed to the edge computing terminal platform and the TensorRT engine is used to accelerate model inference. Finally, experiments are carried out based on the data set of the coal mine comprehensive excavation working face. The experimental results show that on the edge computing terminal platform, the parameters and computational volume of the lightweight FSBD‐YOLOv5s model are reduced by 50.8% and 34.0%, while its detection accuracy and speed reach 94.0% and 43.7 fps, which can fully satisfy the requirements of the accuracy and real‐time for the coal mine engineering applications. |
| format | Article |
| id | doaj-art-dd11183e1d204a409c19ba6a7b2cf228 |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-dd11183e1d204a409c19ba6a7b2cf2282025-08-20T02:12:20ZengWileyIET Image Processing1751-96591751-96672024-10-0118123681369810.1049/ipr2.13206Visual information perception system of coal mine comprehensive excavation working face for edge computing terminalDongyang Zhao0Guoyong Su1Pengyu Wang2State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaState Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Huainan ChinaAbstract Aiming at the problems of low detection accuracy, high computational complexity and long‐time consumption of visual perception model in a complex mining environment, this research designs a visual information perception system of coal mine comprehensive excavation working face for an edge computing terminal. Firstly, the C3‐Fast feature extraction module, spatial pyramid pooling with cross‐stage partial connection (SPPCSPC) pooling module, bi‐directional feature pyramid network and lightweight decoupled detection head are used to optimize the YOLOv5s model, so as to construct the FSBD‐YOLOv5s multi‐object detection model. Secondly, the pruning and distillation algorithm is used to lighten the FSBD‐YOLOv5s model, and the model complexity is greatly reduced while maintaining the model detection accuracy. Further, the lightweight FSBD‐YOLOv5s model is migrated and deployed to the edge computing terminal platform and the TensorRT engine is used to accelerate model inference. Finally, experiments are carried out based on the data set of the coal mine comprehensive excavation working face. The experimental results show that on the edge computing terminal platform, the parameters and computational volume of the lightweight FSBD‐YOLOv5s model are reduced by 50.8% and 34.0%, while its detection accuracy and speed reach 94.0% and 43.7 fps, which can fully satisfy the requirements of the accuracy and real‐time for the coal mine engineering applications.https://doi.org/10.1049/ipr2.13206computer visionconvolutional neural netsembedded systemsfeature extractionimage recognitionobject detection |
| spellingShingle | Dongyang Zhao Guoyong Su Pengyu Wang Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal IET Image Processing computer vision convolutional neural nets embedded systems feature extraction image recognition object detection |
| title | Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| title_full | Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| title_fullStr | Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| title_full_unstemmed | Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| title_short | Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| title_sort | visual information perception system of coal mine comprehensive excavation working face for edge computing terminal |
| topic | computer vision convolutional neural nets embedded systems feature extraction image recognition object detection |
| url | https://doi.org/10.1049/ipr2.13206 |
| work_keys_str_mv | AT dongyangzhao visualinformationperceptionsystemofcoalminecomprehensiveexcavationworkingfaceforedgecomputingterminal AT guoyongsu visualinformationperceptionsystemofcoalminecomprehensiveexcavationworkingfaceforedgecomputingterminal AT pengyuwang visualinformationperceptionsystemofcoalminecomprehensiveexcavationworkingfaceforedgecomputingterminal |