Small object detection in complex open-pit mine backgrounds based on improved YOLOv11
Small object detection in open-pit mines faces challenges such as wide viewing angles and long detection distances, which result in small target imaging. Existing object detection models suffer from feature attenuation caused by progressive image downsampling operations. To address this issue, an im...
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Editorial Department of Industry and Mine Automation
2025-04-01
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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.2025020018 |
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| author | ZHU Yongjun CAI Guangqi HAN Jin MIAO Yanzi MA Xiaoping JIAO Wenhua |
| author_facet | ZHU Yongjun CAI Guangqi HAN Jin MIAO Yanzi MA Xiaoping JIAO Wenhua |
| author_sort | ZHU Yongjun |
| collection | DOAJ |
| description | Small object detection in open-pit mines faces challenges such as wide viewing angles and long detection distances, which result in small target imaging. Existing object detection models suffer from feature attenuation caused by progressive image downsampling operations. To address this issue, an improved YOLOv11 model was proposed and applied to small object detection under complex backgrounds in open-pit mines. The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. A Small Target Feature Enhancement Neck (STFEN) network was designed to replace the original feature pyramid structure in the neck, incorporating a cross-stage partial fusion module to integrate feature maps from different levels. The original CIoU loss function was replaced with the Powerful-IoU (PIoU) loss function to solve the anchor box expansion issue during training, enabling the model to rapidly and accurately focus on small targets. Experimental results on a small object dataset from open-pit mining areas showed that: ① the RFD module reduced model parameters while increasing mAP by 1.5%. Although the STFEN network increased the number of parameters, it improved mAP by 2.2%. The PIoU loss function improved mAP by 1.7% without changing the number of parameters or FLOPs. The combination of all three led to a total mAP improvement of 3.9%. ② The improved YOLOv11 model achieved higher accuracy while maintaining a high inference speed, with mAP improvements of 2.6%, 1.5%, 0.9%, and 2.2% over YOLOv5m, YOLOv8m, YOLOv11m, and RtDetr-L, respectively, and with fewer parameters, making it more suitable for edge deployment. |
| format | Article |
| id | doaj-art-ac90e7575b95491980df8e69f5cdf3d6 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-ac90e7575b95491980df8e69f5cdf3d62025-08-20T03:33:42ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-04-01514939910.13272/j.issn.1671-251x.2025020018Small object detection in complex open-pit mine backgrounds based on improved YOLOv11ZHU Yongjun0CAI Guangqi1HAN Jin2MIAO YanziMA Xiaoping3JIAO WenhuaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaChina Coal Pingshuo Group Co., Ltd., Shuozhou 036006, ChinaChina Coal Pingshuo Group Co., Ltd., Shuozhou 036006, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSmall object detection in open-pit mines faces challenges such as wide viewing angles and long detection distances, which result in small target imaging. Existing object detection models suffer from feature attenuation caused by progressive image downsampling operations. To address this issue, an improved YOLOv11 model was proposed and applied to small object detection under complex backgrounds in open-pit mines. The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. A Small Target Feature Enhancement Neck (STFEN) network was designed to replace the original feature pyramid structure in the neck, incorporating a cross-stage partial fusion module to integrate feature maps from different levels. The original CIoU loss function was replaced with the Powerful-IoU (PIoU) loss function to solve the anchor box expansion issue during training, enabling the model to rapidly and accurately focus on small targets. Experimental results on a small object dataset from open-pit mining areas showed that: ① the RFD module reduced model parameters while increasing mAP by 1.5%. Although the STFEN network increased the number of parameters, it improved mAP by 2.2%. The PIoU loss function improved mAP by 1.7% without changing the number of parameters or FLOPs. The combination of all three led to a total mAP improvement of 3.9%. ② The improved YOLOv11 model achieved higher accuracy while maintaining a high inference speed, with mAP improvements of 2.6%, 1.5%, 0.9%, and 2.2% over YOLOv5m, YOLOv8m, YOLOv11m, and RtDetr-L, respectively, and with fewer parameters, making it more suitable for edge deployment.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025020018open-pit minesmall object detectionyolov11robust feature downsamplingsmall target feature enhancement neckpiou loss function |
| spellingShingle | ZHU Yongjun CAI Guangqi HAN Jin MIAO Yanzi MA Xiaoping JIAO Wenhua Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 Gong-kuang zidonghua open-pit mine small object detection yolov11 robust feature downsampling small target feature enhancement neck piou loss function |
| title | Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 |
| title_full | Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 |
| title_fullStr | Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 |
| title_full_unstemmed | Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 |
| title_short | Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 |
| title_sort | small object detection in complex open pit mine backgrounds based on improved yolov11 |
| topic | open-pit mine small object detection yolov11 robust feature downsampling small target feature enhancement neck piou loss function |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025020018 |
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