Improved YOLOv8s-based foreign object detection method for mine conveyor belts
In low-illumination mine environments, conveyor belt foreign object detection algorithms suffer from insufficient extraction of global image features and an excessive number of model parameters. A method for detecting foreign objects on mine conveyor belts based on an improved version of YOLOv8s was...
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Editorial Department of Industry and Mine Automation
2025-06-01
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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.2025040068 |
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| _version_ | 1849414768229613568 |
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| author | LI Runze GUO Xingge YANG Fazhan ZHAO Peipei XIE Guolong |
| author_facet | LI Runze GUO Xingge YANG Fazhan ZHAO Peipei XIE Guolong |
| author_sort | LI Runze |
| collection | DOAJ |
| description | In low-illumination mine environments, conveyor belt foreign object detection algorithms suffer from insufficient extraction of global image features and an excessive number of model parameters. A method for detecting foreign objects on mine conveyor belts based on an improved version of YOLOv8s was proposed. YOLOv8s was improved using VMamba and MobileNetv4: MobileNetv4 was employed to enhance the backbone network by integrating the Universal Inverted Bottleneck (UIB) module. The efficient inverted residual structure reduced the overall number of model parameters, and a dynamic feature adaptation mechanism was used to strengthen feature robustness in small-object scenarios. The core feature extraction and fusion module C2f was improved by VMamba's Visual State Space (VSS) module, which efficiently captured global contextual information in images through a state space model and four-directional scanning mechanism, enhancing the model’s understanding of global image structure. A parameter-sharing lightweight detection head was designed, using Group Normalization (GN) as the basic convolutional normalization block to compensate for accuracy loss caused by model lightweighting. Experimental results showed that the improved YOLOv8s model achieved an mAP@0.5 of 0.921 and an mAP@0.5:0.95 of 0.601 on a self-built dataset, reduced the number of parameters by 27.7% compared to original YOLOv8s, outperformed mainstream object detection models such as YOLOv11s and YOLOv10s, and met the requirements for foreign object detection on mine conveyor belts. |
| format | Article |
| id | doaj-art-16407d3f33464f12bef6ca912e923762 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-16407d3f33464f12bef6ca912e9237622025-08-20T03:33:43ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-06-015169610410.13272/j.issn.1671-251x.2025040068Improved YOLOv8s-based foreign object detection method for mine conveyor beltsLI Runze0GUO Xingge1YANG Fazhan2ZHAO Peipei3XIE Guolong4School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaChangzhou Haitu Information Technology Co., Ltd., Changzhou 213000, ChinaIn low-illumination mine environments, conveyor belt foreign object detection algorithms suffer from insufficient extraction of global image features and an excessive number of model parameters. A method for detecting foreign objects on mine conveyor belts based on an improved version of YOLOv8s was proposed. YOLOv8s was improved using VMamba and MobileNetv4: MobileNetv4 was employed to enhance the backbone network by integrating the Universal Inverted Bottleneck (UIB) module. The efficient inverted residual structure reduced the overall number of model parameters, and a dynamic feature adaptation mechanism was used to strengthen feature robustness in small-object scenarios. The core feature extraction and fusion module C2f was improved by VMamba's Visual State Space (VSS) module, which efficiently captured global contextual information in images through a state space model and four-directional scanning mechanism, enhancing the model’s understanding of global image structure. A parameter-sharing lightweight detection head was designed, using Group Normalization (GN) as the basic convolutional normalization block to compensate for accuracy loss caused by model lightweighting. Experimental results showed that the improved YOLOv8s model achieved an mAP@0.5 of 0.921 and an mAP@0.5:0.95 of 0.601 on a self-built dataset, reduced the number of parameters by 27.7% compared to original YOLOv8s, outperformed mainstream object detection models such as YOLOv11s and YOLOv10s, and met the requirements for foreign object detection on mine conveyor belts.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025040068conveyor belt foreign object detectionyolov8svmambamobilenetv4lightweightgroup normalization |
| spellingShingle | LI Runze GUO Xingge YANG Fazhan ZHAO Peipei XIE Guolong Improved YOLOv8s-based foreign object detection method for mine conveyor belts Gong-kuang zidonghua conveyor belt foreign object detection yolov8s vmamba mobilenetv4 lightweight group normalization |
| title | Improved YOLOv8s-based foreign object detection method for mine conveyor belts |
| title_full | Improved YOLOv8s-based foreign object detection method for mine conveyor belts |
| title_fullStr | Improved YOLOv8s-based foreign object detection method for mine conveyor belts |
| title_full_unstemmed | Improved YOLOv8s-based foreign object detection method for mine conveyor belts |
| title_short | Improved YOLOv8s-based foreign object detection method for mine conveyor belts |
| title_sort | improved yolov8s based foreign object detection method for mine conveyor belts |
| topic | conveyor belt foreign object detection yolov8s vmamba mobilenetv4 lightweight group normalization |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025040068 |
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