Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n
To address the challenges in underground unmanned locomotive image feature extraction—such as poor lighting, high noise, and motion blur, which result in the loss of image details and difficulty in identifying small targets—a multi-object detection model for underground unmanned locomotives based on...
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| Format: | Article |
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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.2024100036 |
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| author | XU Jinhui WANG Wenshan WANG Shuang WANG Wenyue ZHAO Tingting |
| author_facet | XU Jinhui WANG Wenshan WANG Shuang WANG Wenyue ZHAO Tingting |
| author_sort | XU Jinhui |
| collection | DOAJ |
| description | To address the challenges in underground unmanned locomotive image feature extraction—such as poor lighting, high noise, and motion blur, which result in the loss of image details and difficulty in identifying small targets—a multi-object detection model for underground unmanned locomotives based on DYCS-YOLOv8n was proposed. Based on YOLOv8n, the Convolutional Block Attention Module (CBAM) was introduced, enhancing the extraction of key features through spatial and channel attention mechanisms. A small-object detection layer was added, increasing the original three layers to four, thereby improving the extraction of fine features and enhancing detection performance for small-sized targets. The dynamic upsampling operator DySample was employed to adaptively adjust the sampling strategy according to the input features, better preserving edges and local details in the images and avoiding the loss of critical information. Experiments conducted on a self-constructed underground unmanned locomotive dataset showed that: ① The DYCS-YOLOv8n model achieved a mean Average Precision (mAP@0.5) of 97.5%, an improvement of 3.4% over the YOLOv8n model, with a detection speed of 46.35 frames per second, meeting the requirements for real-time detection. ② Compared with mainstream YOLO series object detection models, DYCS-YOLOv8n achieved the optimal mAP@0.5, maintaining a lightweight structure while ensuring high computational speed. ③ In complex underground scenarios with noise and low illumination, the DYCS-YOLOv8n model exhibited high average detection confidence for pedestrians, tracks, and signal lights, with no cases of missed or false detections. |
| format | Article |
| id | doaj-art-d2f2dee69c144fe583b4eb2b67d72b4f |
| 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-d2f2dee69c144fe583b4eb2b67d72b4f2025-08-20T03:33:42ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-04-015148692, 13010.13272/j.issn.1671-251x.2024100036Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8nXU JinhuiWANG WenshanWANG ShuangWANG WenyueZHAO TingtingTo address the challenges in underground unmanned locomotive image feature extraction—such as poor lighting, high noise, and motion blur, which result in the loss of image details and difficulty in identifying small targets—a multi-object detection model for underground unmanned locomotives based on DYCS-YOLOv8n was proposed. Based on YOLOv8n, the Convolutional Block Attention Module (CBAM) was introduced, enhancing the extraction of key features through spatial and channel attention mechanisms. A small-object detection layer was added, increasing the original three layers to four, thereby improving the extraction of fine features and enhancing detection performance for small-sized targets. The dynamic upsampling operator DySample was employed to adaptively adjust the sampling strategy according to the input features, better preserving edges and local details in the images and avoiding the loss of critical information. Experiments conducted on a self-constructed underground unmanned locomotive dataset showed that: ① The DYCS-YOLOv8n model achieved a mean Average Precision (mAP@0.5) of 97.5%, an improvement of 3.4% over the YOLOv8n model, with a detection speed of 46.35 frames per second, meeting the requirements for real-time detection. ② Compared with mainstream YOLO series object detection models, DYCS-YOLOv8n achieved the optimal mAP@0.5, maintaining a lightweight structure while ensuring high computational speed. ③ In complex underground scenarios with noise and low illumination, the DYCS-YOLOv8n model exhibited high average detection confidence for pedestrians, tracks, and signal lights, with no cases of missed or false detections.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024100036underground unmanned drivinglocomotivemulti-object detectionyolov8nconvolutional attention mechanismsmall-object detectiondynamic upsampling |
| spellingShingle | XU Jinhui WANG Wenshan WANG Shuang WANG Wenyue ZHAO Tingting Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n Gong-kuang zidonghua underground unmanned driving locomotive multi-object detection yolov8n convolutional attention mechanism small-object detection dynamic upsampling |
| title | Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n |
| title_full | Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n |
| title_fullStr | Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n |
| title_full_unstemmed | Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n |
| title_short | Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n |
| title_sort | multi object detection for underground unmanned locomotives based on dycs yolov8n |
| topic | underground unmanned driving locomotive multi-object detection yolov8n convolutional attention mechanism small-object detection dynamic upsampling |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024100036 |
| work_keys_str_mv | AT xujinhui multiobjectdetectionforundergroundunmannedlocomotivesbasedondycsyolov8n AT wangwenshan multiobjectdetectionforundergroundunmannedlocomotivesbasedondycsyolov8n AT wangshuang multiobjectdetectionforundergroundunmannedlocomotivesbasedondycsyolov8n AT wangwenyue multiobjectdetectionforundergroundunmannedlocomotivesbasedondycsyolov8n AT zhaotingting multiobjectdetectionforundergroundunmannedlocomotivesbasedondycsyolov8n |