Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes
With the advancement of intelligent mining, automated drill rod counting based on computer vision has become a crucial means to improve mining efficiency and safety. However, challenges such as dim lighting, small target sizes, diverse object perspectives, and complex visual interference in coal min...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025029391 |
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| author | Fukai Zhang Shuo Zhao Haiyan Zhang Yongqiang Ma Qiang Zhang Shaopu Wang Wenjing Chang |
| author_facet | Fukai Zhang Shuo Zhao Haiyan Zhang Yongqiang Ma Qiang Zhang Shaopu Wang Wenjing Chang |
| author_sort | Fukai Zhang |
| collection | DOAJ |
| description | With the advancement of intelligent mining, automated drill rod counting based on computer vision has become a crucial means to improve mining efficiency and safety. However, challenges such as dim lighting, small target sizes, diverse object perspectives, and complex visual interference in coal mine environments significantly limit the accuracy and real-time performance of existing object detection methods. To address these issues, this paper proposes Drill-oriented Network (DrillNet), a multi-scale fusion network for counting drill rod under complex coal mine conditions using oriented object detection. The model begins by creating and annotating the LDDATA dataset. DrillNet comprises two main components: the YOLO with Multi-Scale Global Context Aggregation Network (YOLO-GC) and the Drill-Count module.The core architecture of YOLO-GC integrates the WaveletPool module, C2f-EMSCP feature extraction unit, GCFPN global context fusion pyramid network, and the oriented bounding box detection head (OBBHead), thereby effectively tackling the issues of insufficient detection accuracy and robustness in challenging coal mine scenarios. Training and testing on the custom LDDATA dataset showed that the YOLO-GC-n model achieved precision and mean average precision (mAP) of 86.9 % and 79.4 %, respectively, surpassing YOLOv10n by 12.6 % in mAP and YOLOv11n-obb by 2.8 %. Finally, the Drill-Count method in DrillNet was tested across multiple views, including drilling and withdrawal phases, with experimental results showing an average drill rod counting accuracy of 98.5 %, while achieving a processing speed of 76 frames per second (FPS), meeting real-time performance requirements. |
| format | Article |
| id | doaj-art-7cfd6e56cf074c27a57f20b20b0d421d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-7cfd6e56cf074c27a57f20b20b0d421d2025-08-26T04:14:30ZengElsevierResults in Engineering2590-12302025-09-012710687610.1016/j.rineng.2025.106876Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenesFukai Zhang0Shuo Zhao1Haiyan Zhang2Yongqiang Ma3Qiang Zhang4Shaopu Wang5Wenjing Chang6School of Software, Henan Polytechnic University, Jiaozuo 454000, China; State Key Laboratory Cultivation Base for Gas Geology and Gas Control, Henan Polytechnic University, Jiaozuo 454000, China; Corresponding author.School of Software, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Software, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Software, Henan Polytechnic University, Jiaozuo 454000, ChinaTechnology R&D Center, Shanxi Technology Institute of Jincheng Anthracite Mining Group Co., LTD., Jincheng 048006, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Software, Henan Polytechnic University, Jiaozuo 454000, ChinaWith the advancement of intelligent mining, automated drill rod counting based on computer vision has become a crucial means to improve mining efficiency and safety. However, challenges such as dim lighting, small target sizes, diverse object perspectives, and complex visual interference in coal mine environments significantly limit the accuracy and real-time performance of existing object detection methods. To address these issues, this paper proposes Drill-oriented Network (DrillNet), a multi-scale fusion network for counting drill rod under complex coal mine conditions using oriented object detection. The model begins by creating and annotating the LDDATA dataset. DrillNet comprises two main components: the YOLO with Multi-Scale Global Context Aggregation Network (YOLO-GC) and the Drill-Count module.The core architecture of YOLO-GC integrates the WaveletPool module, C2f-EMSCP feature extraction unit, GCFPN global context fusion pyramid network, and the oriented bounding box detection head (OBBHead), thereby effectively tackling the issues of insufficient detection accuracy and robustness in challenging coal mine scenarios. Training and testing on the custom LDDATA dataset showed that the YOLO-GC-n model achieved precision and mean average precision (mAP) of 86.9 % and 79.4 %, respectively, surpassing YOLOv10n by 12.6 % in mAP and YOLOv11n-obb by 2.8 %. Finally, the Drill-Count method in DrillNet was tested across multiple views, including drilling and withdrawal phases, with experimental results showing an average drill rod counting accuracy of 98.5 %, while achieving a processing speed of 76 frames per second (FPS), meeting real-time performance requirements.http://www.sciencedirect.com/science/article/pii/S2590123025029391Drill rod countingOriented object detectionSalient peaksDeep learningDrill site recognition |
| spellingShingle | Fukai Zhang Shuo Zhao Haiyan Zhang Yongqiang Ma Qiang Zhang Shaopu Wang Wenjing Chang Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes Results in Engineering Drill rod counting Oriented object detection Salient peaks Deep learning Drill site recognition |
| title | Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| title_full | Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| title_fullStr | Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| title_full_unstemmed | Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| title_short | Multi-scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| title_sort | multi scale fusion network for coal mine drill rod counting based on directional object detection in complex scenes |
| topic | Drill rod counting Oriented object detection Salient peaks Deep learning Drill site recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025029391 |
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