A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images
Rapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining. In this paper, a novel coal-rock recognition method is proposed based on fusing laser point cloud and images, named Multi-Modal Frustum PointNet (MMFP). Firstly, MobileNetV3 is used as th...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | International Journal of Mining Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095268625000928 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229293626851328 |
|---|---|
| author | Yang Liu Lei Si Zhongbin Wang Miao Chen Xin Li Dong Wei Jinheng Gu |
| author_facet | Yang Liu Lei Si Zhongbin Wang Miao Chen Xin Li Dong Wei Jinheng Gu |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Rapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining. In this paper, a novel coal-rock recognition method is proposed based on fusing laser point cloud and images, named Multi-Modal Frustum PointNet (MMFP). Firstly, MobileNetV3 is used as the backbone network of Mask R-CNN to reduce the network parameters and compress the model volume. The dilated convolutional block attention mechanism (Dilated CBAM) and inception structure are combined with MobileNetV3 to further enhance the detection accuracy. Subsequently, the 2D target candidate box is calculated through the improved Mask R-CNN, and the frustum point cloud in the 2D target candidate box is extracted to reduce the calculation scale and spatial search range. Then, the self-attention PointNet is constructed to segment the fused point cloud within the frustum range, and the bounding box regression network is used to predict the bounding box parameters. Finally, an experimental platform of shearer coal wall cutting is established, and multiple comparative experiments are conducted. Experimental results indicate that the proposed coal-rock recognition method is superior to other advanced models. |
| format | Article |
| id | doaj-art-33d33ea46a814660a071f56017020967 |
| institution | Kabale University |
| issn | 2095-2686 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Mining Science and Technology |
| spelling | doaj-art-33d33ea46a814660a071f560170209672025-08-22T04:56:07ZengElsevierInternational Journal of Mining Science and Technology2095-26862025-07-013571057107110.1016/j.ijmst.2025.05.009A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and imagesYang Liu0Lei Si1Zhongbin Wang2Miao Chen3Xin Li4Dong Wei5Jinheng Gu6School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou 221116, China; Corresponding author.School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou 221116, ChinaRapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining. In this paper, a novel coal-rock recognition method is proposed based on fusing laser point cloud and images, named Multi-Modal Frustum PointNet (MMFP). Firstly, MobileNetV3 is used as the backbone network of Mask R-CNN to reduce the network parameters and compress the model volume. The dilated convolutional block attention mechanism (Dilated CBAM) and inception structure are combined with MobileNetV3 to further enhance the detection accuracy. Subsequently, the 2D target candidate box is calculated through the improved Mask R-CNN, and the frustum point cloud in the 2D target candidate box is extracted to reduce the calculation scale and spatial search range. Then, the self-attention PointNet is constructed to segment the fused point cloud within the frustum range, and the bounding box regression network is used to predict the bounding box parameters. Finally, an experimental platform of shearer coal wall cutting is established, and multiple comparative experiments are conducted. Experimental results indicate that the proposed coal-rock recognition method is superior to other advanced models.http://www.sciencedirect.com/science/article/pii/S2095268625000928Coal mining faceCoal-rock recognitionDeep learningLaser point cloud and images fusionMulti-Modal Frustum PointNet (MMFP) |
| spellingShingle | Yang Liu Lei Si Zhongbin Wang Miao Chen Xin Li Dong Wei Jinheng Gu A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images International Journal of Mining Science and Technology Coal mining face Coal-rock recognition Deep learning Laser point cloud and images fusion Multi-Modal Frustum PointNet (MMFP) |
| title | A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images |
| title_full | A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images |
| title_fullStr | A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images |
| title_full_unstemmed | A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images |
| title_short | A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images |
| title_sort | novel coal rock recognition method in coal mining face based on fusing laser point cloud and images |
| topic | Coal mining face Coal-rock recognition Deep learning Laser point cloud and images fusion Multi-Modal Frustum PointNet (MMFP) |
| url | http://www.sciencedirect.com/science/article/pii/S2095268625000928 |
| work_keys_str_mv | AT yangliu anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT leisi anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT zhongbinwang anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT miaochen anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT xinli anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT dongwei anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT jinhenggu anovelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT yangliu novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT leisi novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT zhongbinwang novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT miaochen novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT xinli novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT dongwei novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages AT jinhenggu novelcoalrockrecognitionmethodincoalminingfacebasedonfusinglaserpointcloudandimages |