Multi-target detection of underground personnel based on an improved YOLOv8n model
This study aims to address the complex challenges in monitoring underground personnel in hazardous areas, including uneven lighting, target scale inconsistency, and occlusion. An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. The alg...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2025-01-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110035 |
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| author | WEN Yongzhong JIA Pengtao XIA Mingao ZHANG Longgang WANG Weifeng |
| author_facet | WEN Yongzhong JIA Pengtao XIA Mingao ZHANG Longgang WANG Weifeng |
| author_sort | WEN Yongzhong |
| collection | DOAJ |
| description | This study aims to address the complex challenges in monitoring underground personnel in hazardous areas, including uneven lighting, target scale inconsistency, and occlusion. An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. The algorithm modified the Neck layer by incorporating a Multi-Scale Spatially Enhanced Attention Mechanism (MultiSEAM) to enhance the detection of occluded targets. Furthermore, a Hybrid Local Channel Attention (MLCA) mechanism was introduced into the C2f module to create the C2f-MLCA module, which fused local and global feature information, thereby improving feature representation. An Adaptive Spatial Feature Fusion (ASFF) module was embedded in the Head layer to boost detection performance for small-scale targets. Experimental results demonstrated that YOLOv8n-ASAM outperformed mainstream models such as Faster R-CNN, SSD, RT-DETR, YOLOv5s, and YOLOv7 in terms of overall performance, achieving mAP@0.5 and mAP@0.5: 0.95 of 93.4% and 60.1%, respectively,with a speed of 80.0 frames per second,the parameter is 5.80×106, effectively balancing accuracy and complexity. Moreover, YOLOv8n-ASAM exhibited superior performance under uneven lighting, target scale inconsistency, and occlusion, making it well-suited for real-world applications. |
| format | Article |
| id | doaj-art-e4dea7c52d594cf9b1e321e196336373 |
| institution | OA Journals |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-e4dea7c52d594cf9b1e321e1963363732025-08-20T02:13:03ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-01-015113137, 7710.13272/j.issn.1671-251x.2024110035Multi-target detection of underground personnel based on an improved YOLOv8n modelWEN Yongzhong0JIA Pengtao1XIA Mingao2ZHANG Longgang3WANG Weifeng4Shaanxi Shanmei Pubai Mining Co., Ltd., Weinan 715517, ChinaCollege of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, ChinaCollege of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, ChinaShaanxi Shanmei Pubai Mining Co., Ltd., Weinan 715517, ChinaCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, ChinaThis study aims to address the complex challenges in monitoring underground personnel in hazardous areas, including uneven lighting, target scale inconsistency, and occlusion. An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. The algorithm modified the Neck layer by incorporating a Multi-Scale Spatially Enhanced Attention Mechanism (MultiSEAM) to enhance the detection of occluded targets. Furthermore, a Hybrid Local Channel Attention (MLCA) mechanism was introduced into the C2f module to create the C2f-MLCA module, which fused local and global feature information, thereby improving feature representation. An Adaptive Spatial Feature Fusion (ASFF) module was embedded in the Head layer to boost detection performance for small-scale targets. Experimental results demonstrated that YOLOv8n-ASAM outperformed mainstream models such as Faster R-CNN, SSD, RT-DETR, YOLOv5s, and YOLOv7 in terms of overall performance, achieving mAP@0.5 and mAP@0.5: 0.95 of 93.4% and 60.1%, respectively,with a speed of 80.0 frames per second,the parameter is 5.80×106, effectively balancing accuracy and complexity. Moreover, YOLOv8n-ASAM exhibited superior performance under uneven lighting, target scale inconsistency, and occlusion, making it well-suited for real-world applications.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110035underground hazardous areas in coal minesmulti-target detection of underground personnelyolov8nmulti-scale spatially enhanced attention mechanismadaptive spatial feature fusionlightweight hybrid local channel attention mechanism |
| spellingShingle | WEN Yongzhong JIA Pengtao XIA Mingao ZHANG Longgang WANG Weifeng Multi-target detection of underground personnel based on an improved YOLOv8n model Gong-kuang zidonghua underground hazardous areas in coal mines multi-target detection of underground personnel yolov8n multi-scale spatially enhanced attention mechanism adaptive spatial feature fusion lightweight hybrid local channel attention mechanism |
| title | Multi-target detection of underground personnel based on an improved YOLOv8n model |
| title_full | Multi-target detection of underground personnel based on an improved YOLOv8n model |
| title_fullStr | Multi-target detection of underground personnel based on an improved YOLOv8n model |
| title_full_unstemmed | Multi-target detection of underground personnel based on an improved YOLOv8n model |
| title_short | Multi-target detection of underground personnel based on an improved YOLOv8n model |
| title_sort | multi target detection of underground personnel based on an improved yolov8n model |
| topic | underground hazardous areas in coal mines multi-target detection of underground personnel yolov8n multi-scale spatially enhanced attention mechanism adaptive spatial feature fusion lightweight hybrid local channel attention mechanism |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024110035 |
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