{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:15:21Z","timestamp":1773519321248,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Xi\u2019an Science and Technology Program","award":["23ZDCYJSGG0025-2022"],"award-info":[{"award-number":["23ZDCYJSGG0025-2022"]}]},{"name":"General Project of Science and Technology Department of Shaanxi Province","award":["2021JQ-574"],"award-info":[{"award-number":["2021JQ-574"]}]},{"name":"Science and Technology Innovation Fund Special Project of Tiandi (Changzhou) Automation Co., Ltd.","award":["2022TY2012"],"award-info":[{"award-number":["2022TY2012"]}]},{"name":"Science ResearchProgram of Shaanxi Educational Committee under Grant","award":["23JC049"],"award-info":[{"award-number":["23JC049"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11554-024-01466-0","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T03:14:32Z","timestamp":1715570072000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A real-time detection for miner behavior via DYS-YOLOv8n model"],"prefix":"10.1007","volume":"21","author":[{"given":"Fangfang","family":"Xin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoxiu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongguang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"1466_CR1","unstructured":"Xiaobin, Y., Shilu, Z., Na, L.I., Xiaoyao, W.: Deep learning and its application in coal mine safety. Safety in Coal Mines (2019)"},{"key":"1466_CR2","doi-asserted-by":"crossref","unstructured":"Wu, B., Wang, J., Zhong, M., Xu, C., Qu, B.: Multidimensional analysis of coal mine safety accidents in china\u201470 years review. In: Mining, Metallurgy & Exploration, pp. 1\u201310 (2022)","DOI":"10.1007\/s42461-022-00722-w"},{"issue":"6","key":"1466_CR3","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1466_CR4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Computer vision & pattern recognition (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1466_CR5","doi-asserted-by":"crossref","unstructured":"Farhadi, A., Redmon, J.: Yolo9000: better, faster, stronger (2016)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1466_CR6","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv e-prints (2018)"},{"key":"1466_CR7","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection (2020)"},{"key":"1466_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1466_CR9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"1466_CR10","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017)"},{"key":"1466_CR11","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"1466_CR12","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021 (2021)"},{"key":"1466_CR13","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, C., Wang, P., Wei, H., Huang, S., Li, H.: Unsafe mining behavior identification method based on an improved st-gcn. Sustainability 15(2) (2023)","DOI":"10.3390\/su15021041"},{"key":"1466_CR14","doi-asserted-by":"crossref","unstructured":"Shi, X., Huang, J., Huang, B.: An underground abnormal behavior recognition method based on an optimized alphapose-st-gcn. J. Circuits Syst. Comput. (2022)","DOI":"10.1142\/S0218126622502140"},{"key":"1466_CR15","doi-asserted-by":"crossref","unstructured":"Liu, S., Bai, X., Fang, M., Li, L., Hung, C.C.: Mixed graph convolution and residual transformation network for skeleton-based action recognition. Appl. Intell. 1\u201312 (2021)","DOI":"10.1007\/s10489-021-02517-w"},{"key":"1466_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Zeng, W., Xing, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"1466_CR17","first-page":"8","volume":"8","author":"H Yang","year":"2020","unstructured":"Yang, H., Gu, Y., Zhu, J., Hu, K., Zhang, X.: Pgcn-tca: pseudo graph convolutional network with temporal and channel-wise attention for skeleton-based action recognition. IEEE Access 8, 8 (2020)","journal-title":"IEEE Access"},{"key":"1466_CR18","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition (2018)","DOI":"10.1109\/CVPR.2019.01230"},{"issue":"15","key":"1466_CR19","doi-asserted-by":"publisher","first-page":"8584","DOI":"10.3390\/app13158584","volume":"13","author":"R Rijayanti","year":"2023","unstructured":"Rijayanti, R., Hwang, M., Jin, K.: Detection of anomalous behavior of manufacturing workers using deep learning-based recognition of human-object interaction. Appl. Sci. 13(15), 8584 (2023)","journal-title":"Appl. Sci."},{"issue":"19","key":"1466_CR20","doi-asserted-by":"publisher","first-page":"10700","DOI":"10.3390\/app131910700","volume":"13","author":"X Li","year":"2023","unstructured":"Li, X., Hao, T., Li, F., Zhao, L., Wang, Z.: Faster r-cnn-lstm construction site unsafe behavior recognition model. Appl. Sci. 13(19), 10700 (2023)","journal-title":"Appl. Sci."},{"issue":"21","key":"1466_CR21","doi-asserted-by":"publisher","first-page":"8794","DOI":"10.3390\/s23218794","volume":"23","author":"W Yao","year":"2023","unstructured":"Yao, W., Wang, A., Nie, Y., Lv, Z., Nie, S., Huang, C., Liu, Z.: Study on the recognition of coal miners\u2019 unsafe behavior and status in the hoist cage based on machine vision. Sensors 23(21), 8794 (2023)","journal-title":"Sensors"},{"key":"1466_CR22","doi-asserted-by":"publisher","first-page":"101988","DOI":"10.1016\/j.aei.2023.101988","volume":"56","author":"L Li","year":"2023","unstructured":"Li, L., Zhang, P., Yang, S., Jiao, W.: Yolov5-sfe: an algorithm fusing spatio-temporal features for detecting and recognizing workers\u2019 operating behaviors. Adv. Eng. Inform. 56, 101988 (2023)","journal-title":"Adv. Eng. Inform."},{"issue":"2","key":"1466_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11554-023-01407-3","volume":"21","author":"X Shao","year":"2024","unstructured":"Shao, X., Liu, S., Li, X., Lyu, Z., Li, H.: Rep-yolo: an efficient detection method for mine personnel. J. Real-Time Image Proc. 21(2), 1\u201316 (2024)","journal-title":"J. Real-Time Image Proc."},{"issue":"1","key":"1466_CR24","doi-asserted-by":"publisher","first-page":"013017","DOI":"10.1117\/1.JEI.31.1.013017","volume":"31","author":"X Li","year":"2022","unstructured":"Li, X., Wang, S., Liu, B., Chen, W., Fan, W., Tian, Z.: Improved yolov4 network using infrared images for personnel detection in coal mines. J. Electron. Imaging 31(1), 013017 (2022)","journal-title":"J. Electron. Imaging"},{"issue":"1","key":"1466_CR25","doi-asserted-by":"publisher","first-page":"015410","DOI":"10.1088\/1361-6501\/ad060e","volume":"35","author":"D Zhao","year":"2023","unstructured":"Zhao, D., Guoyong, S., Cheng, G., Wang, P., Chen, W., Yang, Y.: Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine. Meas. Sci. Technol. 35(1), 015410 (2023)","journal-title":"Meas. Sci. Technol."},{"issue":"12","key":"1466_CR26","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.3390\/s22124331","volume":"22","author":"X Zhi","year":"2022","unstructured":"Zhi, X., Li, J., Meng, Y., Zhang, X.: Cap-yolo: channel attention based pruning yolo for coal mine real-time intelligent monitoring. Sensors 22(12), 4331 (2022)","journal-title":"Sensors"},{"key":"1466_CR27","doi-asserted-by":"crossref","unstructured":"Fan, Y., Mao, S., Li, M., Wu, Z., Kang, J.: Cm-yolov8: lightweight yolo for coal mine fully mechanized mining face (2024)","DOI":"10.20944\/preprints202401.1814.v1"},{"key":"1466_CR28","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)"},{"key":"1466_CR29","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. IEEE (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"1466_CR30","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6047\u20136056 (2023)","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"1466_CR31","doi-asserted-by":"crossref","unstructured":"Li, J., Wen, Y., He, L.: Scconv: spatial and channel reconstruction convolution for feature redundancy. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6153\u20136162 (2023)","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"1466_CR32","unstructured":"Ma, S., Yong, X.: Mpdiou: a loss for efficient and accurate bounding box regression (2023)"},{"key":"1466_CR33","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, X., Ma, B., Wang, Y., Wu, Y., Yan, J., Liu, Y., Zhang, C., Wan, J., Wang, Y.: An open dataset for intelligent recognition and classification of abnormal condition in longwall mining. Sci. Data 10(1) (2023)","DOI":"10.1038\/s41597-023-02322-9"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01466-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01466-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01466-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T08:41:31Z","timestamp":1717663291000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01466-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1466"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01466-0","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"12 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"92"}}