An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines
Abstract The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, and others. High-quality datasets are essential for developing and validating artificial intelligence (AI) algorithms in coal mine safety m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05118-1 |
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| author | Pengzhen Zhao Xichao Wang Shuainan Yu Xiangqing Dong Baojiang Li Haiyan Wang Guochu Chen |
| author_facet | Pengzhen Zhao Xichao Wang Shuainan Yu Xiangqing Dong Baojiang Li Haiyan Wang Guochu Chen |
| author_sort | Pengzhen Zhao |
| collection | DOAJ |
| description | Abstract The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, and others. High-quality datasets are essential for developing and validating artificial intelligence (AI) algorithms in coal mine safety monitoring and automation field. Currently, there is no comprehensive benchmark dataset for coal mine industrial scenarios, limiting the research progress of AI algorithms in this industry. For the first time, this study constructed a benchmark dataset (DsDPM 66) specifically for underground coal mine drilling operations, containing 105,096 images obtained from surveillance videos of multiple drilling operation scenes. The dataset has been manually annotated to support computer vision tasks such as object detection and pose estimation. In addition, this study conducted extensive benchmarking experiments on this dataset, applying various advanced AI algorithms including but not limited to YOLOv8 and DETR. The results indicate the proposed dataset highlights areas for improvement in algorithmic models and fills the data gap in the coal mining, providing valuable resources for developing coal mine safety monitoring. |
| format | Article |
| id | doaj-art-703fc75c345146a285632fdfa5bebc4e |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-703fc75c345146a285632fdfa5bebc4e2025-08-20T01:52:03ZengNature PortfolioScientific Data2052-44632025-05-0112111610.1038/s41597-025-05118-1An open paradigm dataset for intelligent monitoring of underground drilling operations in coal minesPengzhen Zhao0Xichao Wang1Shuainan Yu2Xiangqing Dong3Baojiang Li4Haiyan Wang5Guochu Chen6School of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversitySchool of Electrical Engineering, Shanghai DianJi UniversityAbstract The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, and others. High-quality datasets are essential for developing and validating artificial intelligence (AI) algorithms in coal mine safety monitoring and automation field. Currently, there is no comprehensive benchmark dataset for coal mine industrial scenarios, limiting the research progress of AI algorithms in this industry. For the first time, this study constructed a benchmark dataset (DsDPM 66) specifically for underground coal mine drilling operations, containing 105,096 images obtained from surveillance videos of multiple drilling operation scenes. The dataset has been manually annotated to support computer vision tasks such as object detection and pose estimation. In addition, this study conducted extensive benchmarking experiments on this dataset, applying various advanced AI algorithms including but not limited to YOLOv8 and DETR. The results indicate the proposed dataset highlights areas for improvement in algorithmic models and fills the data gap in the coal mining, providing valuable resources for developing coal mine safety monitoring.https://doi.org/10.1038/s41597-025-05118-1 |
| spellingShingle | Pengzhen Zhao Xichao Wang Shuainan Yu Xiangqing Dong Baojiang Li Haiyan Wang Guochu Chen An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines Scientific Data |
| title | An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| title_full | An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| title_fullStr | An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| title_full_unstemmed | An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| title_short | An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| title_sort | open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines |
| url | https://doi.org/10.1038/s41597-025-05118-1 |
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