{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T10:51:46Z","timestamp":1773571906310,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51774176"],"award-info":[{"award-number":["51774176"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A dataset called unsafe actions of underground miners (UAUM) was constructed and included ten categories of such actions. Underground images were enhanced using spatial- and frequency-domain enhancement algorithms. A combination of the YOLOX object detection algorithm and the Lite-HRNet human key-point detection algorithm was utilized to obtain skeleton modal data. The CBAM-PoseC3D model, a skeleton modal action-recognition model incorporating the CBAM attention module, was proposed and combined with the RGB modal feature-extraction model CBAM-SlowOnly. Ultimately, this formed the Convolutional Block Attention Module\u2013Multimodal Feature-Fusion Action Recognition (CBAM-MFFAR) model for recognizing unsafe actions of underground miners. The improved CBAM-MFFAR model achieved a recognition accuracy of 95.8% on the NTU60 RGB+D public dataset under the X-Sub benchmark. Compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, the recognition accuracy was improved by 2%, 2.7%, 7.3%, and 14.3%, respectively. On the UAUM dataset, the CBAM-MFFAR model achieved a recognition accuracy of 94.6%, with improvements of 2.6%, 4%, 12%, and 17.3% compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, respectively. In field validation at mining sites, the CBAM-MFFAR model accurately recognized similar and multiple unsafe actions among underground miners.<\/jats:p>","DOI":"10.3390\/s24144557","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T14:15:49Z","timestamp":1721052949000},"page":"4557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Convolutional Block Attention Module\u2013Multimodal Feature-Fusion Action Recognition: Enabling Miner Unsafe Action Recognition"],"prefix":"10.3390","volume":"24","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China"}]},{"given":"Xiaoqing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China"}]},{"given":"Jiaoqun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China"}]},{"given":"Zengxiang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mining Engineering, University of Science and Technology Liaoning, Anshan 114051, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.psep.2019.10.014","article-title":"Statistical analysis the characteristics of extraordinarily severe coal mine accidents (ESCMAs) in China from 1950 to 2018","volume":"133","author":"Zhang","year":"2020","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, C., Wang, P., Wei, H., Huang, S., and Li, H. (2023). Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN. Sustainability, 15.","DOI":"10.3390\/su15021041"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s40789-022-00491-3","article-title":"Research and practice of intelligent coal mine technology systems in China","volume":"9","author":"Wang","year":"2022","journal-title":"Int. J. Coal Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5837","DOI":"10.3233\/JIFS-189423","article-title":"Design of mine safety dynamic diagnosis system based on cloud computing and internet of things technology","volume":"40","author":"Wang","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.comcom.2019.12.032","article-title":"New insights on ground control in intelligent mining with Internet of Things","volume":"150","author":"Hao","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.eng.2018.05.013","article-title":"Intelligent Mining Technology for an Underground Metal Mine Based on Unmanned Equipment","volume":"4","author":"Li","year":"2018","journal-title":"Engineering"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, C., Deng, J., Su, C., and Gao, Z. (2022). Analysis of Factors Influencing Miners\u2019 Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19127368"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.eswa.2017.09.029","article-title":"Abnormal behavior recognition for intelligent video surveillance systems: A review","volume":"91","author":"Zagrouba","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., and Chen, D.-S. (2019). A Comprehensive Survey of Vision-Based Human Action Recognition Methods. Sensors, 19.","DOI":"10.3390\/s19051005"},{"key":"ref_10","first-page":"2153","article-title":"Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model","volume":"65","author":"Qian","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18787","DOI":"10.1007\/s11042-021-10667-9","article-title":"Abnormal behavior recognition using 3D-CNN combined with LSTM","volume":"80","author":"Guan","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, B., Wang, X., Bao, Q., Jia, B., Li, X., and Wang, Y. (2022). An Unsafe Behavior Detection Method Based on Improved YOLO Framework. Electronics, 11.","DOI":"10.3390\/electronics11121912"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104703","DOI":"10.1016\/j.autcon.2022.104703","article-title":"Transformer-based deep learning model and video dataset for unsafe action identification in construction projects","volume":"146","author":"Yang","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, X., Hao, T., Li, F., Zhao, L., and Wang, Z. (2023). Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model. Appl. Sci., 13.","DOI":"10.3390\/app131910700"},{"key":"ref_15","first-page":"41","article-title":"Identification of miners\u2019 unsafe behaviors based on transfer learning and residual network","volume":"30","author":"Wen","year":"2020","journal-title":"China Saf. Sci. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2250214","DOI":"10.1142\/S0218126622502140","article-title":"An Underground Abnormal Behavior Recognition Method Based on an Optimized Alphapose-ST-GCN","volume":"31","author":"Shi","year":"2022","journal-title":"J. Circuits Syst. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109021","DOI":"10.1016\/j.compeleceng.2023.109021","article-title":"An efficient detection of non-standard miner behavior using improved YOLOv8","volume":"112","author":"Wang","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, Y., Yang, Y., and Duan, S. (2024). Dual-branch deep learning architecture enabling miner behavior recognition. Multimed. Tools Appl., 1\u201316.","DOI":"10.1007\/s11042-024-19164-1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yao, W., Wang, A., Nie, Y., Lv, Z., Nie, S., Huang, C., and Liu, Z. (2023). Study on the Recognition of Coal Miners\u2019 Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision. Sensors, 23.","DOI":"10.3390\/s23218794"},{"key":"ref_20","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (November, January 27). Slowfast networks for video recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_21","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, C., Xiao, B., Gao, C., Yuan, L., Zhang, L., Sang, N., and Wang, J. (2021, January 20\u201325). Lite-hrnet: A lightweight high-resolution network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhao, Y., Chen, K., Lin, D., and Dai, B. (2022, January 18\u201324). Revisiting skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00298"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13640-016-0138-1","article-title":"An adaptive gamma correction for image enhancement","volume":"2016","author":"Rahman","year":"2016","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.dsp.2003.07.002","article-title":"A simple and effective histogram equalization approach to image enhancement","volume":"14","author":"Cheng","year":"2004","journal-title":"Digit. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, J., Ling, Y., and Zheng, X. (2015, January 14\u201316). Forensic detection of Gaussian low-pass filtering in digital images. Proceedings of the 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, China.","DOI":"10.1109\/CISP.2015.7407990"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.-T., and Wang, G. (2016, January 27\u201330). Ntu rgb+ d: A large scale dataset for 3d human activity analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.115"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., and Lu, H. (2019, January 15\u201320). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01230"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4557\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:16:42Z","timestamp":1760109402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,14]]},"references-count":32,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144557"],"URL":"https:\/\/doi.org\/10.3390\/s24144557","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,14]]}}}