RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification
Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognitio...
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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6943 |
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| author | Yutong Wang Ziming Kou Cong Han Yuchen Qin |
| author_facet | Yutong Wang Ziming Kou Cong Han Yuchen Qin |
| author_sort | Yutong Wang |
| collection | DOAJ |
| description | Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential. |
| format | Article |
| id | doaj-art-d298d95aa77f40be96b08dea176fee96 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-d298d95aa77f40be96b08dea176fee962025-08-20T02:14:23ZengMDPI AGSensors1424-82202024-10-012421694310.3390/s24216943RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue IdentificationYutong Wang0Ziming Kou1Cong Han2Yuchen Qin3School of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCoal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential.https://www.mdpi.com/1424-8220/24/21/6943coal gangue recognitionYOLOv8lightweight module RepGhostattention mechanism MCAlow light enhancement |
| spellingShingle | Yutong Wang Ziming Kou Cong Han Yuchen Qin RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification Sensors coal gangue recognition YOLOv8 lightweight module RepGhost attention mechanism MCA low light enhancement |
| title | RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification |
| title_full | RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification |
| title_fullStr | RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification |
| title_full_unstemmed | RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification |
| title_short | RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification |
| title_sort | rrbm yolo research on efficient and lightweight convolutional neural networks for underground coal gangue identification |
| topic | coal gangue recognition YOLOv8 lightweight module RepGhost attention mechanism MCA low light enhancement |
| url | https://www.mdpi.com/1424-8220/24/21/6943 |
| work_keys_str_mv | AT yutongwang rrbmyoloresearchonefficientandlightweightconvolutionalneuralnetworksforundergroundcoalgangueidentification AT zimingkou rrbmyoloresearchonefficientandlightweightconvolutionalneuralnetworksforundergroundcoalgangueidentification AT conghan rrbmyoloresearchonefficientandlightweightconvolutionalneuralnetworksforundergroundcoalgangueidentification AT yuchenqin rrbmyoloresearchonefficientandlightweightconvolutionalneuralnetworksforundergroundcoalgangueidentification |