{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:18:50Z","timestamp":1770970730897,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"6-7","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11760-024-03231-z","type":"journal-article","created":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T18:01:52Z","timestamp":1714845712000},"page":"5269-5280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ultra-lightweight aerial passenger device safety behavior detection model based on channel spatial interaction and cascade grouping"],"prefix":"10.1007","volume":"18","author":[{"given":"Ruxin","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiquan","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qunpo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"3231_CR1","doi-asserted-by":"publisher","first-page":"102590","DOI":"10.1016\/j.resourpol.2022.102590","volume":"76","author":"W Li","year":"2022","unstructured":"Li, W., Wang, A., Zhong, W., et al.: The role of mineral-related industries in Chinese industrial pattern. Resour. Policy 76, 102590 (2022). https:\/\/doi.org\/10.1016\/j.resourpol.2022.102590","journal-title":"Resour. Policy"},{"key":"3231_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2022.07.016","author":"S Sadeghi","year":"2022","unstructured":"Sadeghi, S., Soltanmohammadlou, N., Nasirzadeh, F.: Applications of wireless sensor networks to improve occupational safety and health in underground mines. J. Saf. Res. (2022). https:\/\/doi.org\/10.1016\/j.jsr.2022.07.016","journal-title":"J. Saf. Res."},{"issue":"6","key":"3231_CR3","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1016\/j.ijmst.2022.09.004","volume":"32","author":"Y Hua","year":"2022","unstructured":"Hua, Y., Nie, W., Liu, Q., et al.: Analysis of diffusion behavior of harmful emissions from trackless rubber-wheel diesel vehicles in underground coal mines[J]. Int. J. Min. Sci. Technol. 32(6), 1285\u20131299 (2022). https:\/\/doi.org\/10.1016\/j.ijmst.2022.09.004","journal-title":"Int. J. Min. Sci. Technol."},{"issue":"2","key":"3231_CR4","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.dcan.2022.08.002","volume":"9","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Yan, Q., Zhu, X., et al.: Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine. Digital Commun. Netw. 9(2), 296\u2013305 (2023). https:\/\/doi.org\/10.1016\/j.dcan.2022.08.002","journal-title":"Digital Commun. Netw."},{"issue":"9","key":"3231_CR5","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.3390\/s23094294","volume":"23","author":"M Imam","year":"2023","unstructured":"Imam, M., Baina, K., Tabii, Y., et al.: The future of mine safety: a comprehensive review of anti-collision systems based on computer vision in underground mines. Sensors 23(9), 4294 (2023). https:\/\/doi.org\/10.3390\/s23094294","journal-title":"Sensors"},{"issue":"2","key":"3231_CR6","first-page":"10057854","volume":"32","author":"Z Yongqing","year":"2023","unstructured":"Yongqing, Z., Huisong, G., Yongnian, Z., et al.: Innovative design of mining monkey cart seats based on TRIZ and TOC. Min. Metall. 32(2), 10057854 (2023)","journal-title":"Min. Metall."},{"issue":"4","key":"3231_CR7","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.sigpro.2010.08.010","volume":"91","author":"Y Pang","year":"2011","unstructured":"Pang, Y., Yuan, Y., Li, X., et al.: Efficient HOG human detection. Signal Process. 91(4), 773\u2013781 (2011). https:\/\/doi.org\/10.1016\/j.sigpro.2010.08.010","journal-title":"Signal Process."},{"key":"3231_CR8","doi-asserted-by":"publisher","first-page":"104401","DOI":"10.1016\/j.imavis.2022.104401","volume":"120","author":"W Gu","year":"2022","unstructured":"Gu, W., Bai, S., Kong, L.: A review on 2D instance segmentation based on deep neural networks. Image Vis. Comput. 120, 104401 (2022). https:\/\/doi.org\/10.1016\/j.imavis.2022.104401","journal-title":"Image Vis. Comput."},{"key":"3231_CR9","doi-asserted-by":"publisher","unstructured":"Redmon, J., et al.: You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. (2016). https:\/\/doi.org\/10.48550\/arXiv.1506.02640","DOI":"10.48550\/arXiv.1506.02640"},{"key":"3231_CR10","doi-asserted-by":"publisher","unstructured":"Liu, W.: Ssd: Single shot multibox detector. Computer Vision-ECCV. In 2016: 14th European Conference, Amsterdam, The Netherlands, October 1-?14, 2016. Proceedings, Part I 14. Springer International Publishing 2016,(2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"4","key":"3231_CR11","first-page":"200","volume":"55","author":"W Li","year":"2019","unstructured":"Li, W., Wei, C., Wang, L.: Improved faster rcnn approach for pedestrian detection in underground coal mine. Comput. Eng. Appl. 55(4), 200\u2013207 (2019)","journal-title":"Comput. Eng. Appl."},{"issue":"3","key":"3231_CR12","first-page":"700","volume":"51","author":"Z Yanhua","year":"2023","unstructured":"Yanhua, Z., wANG, B.S.: Research on violation detection of underground coal mine bearing devices. Comput. Digital Eng. 51(3), 700\u2013705 (2023)","journal-title":"Comput. Digital Eng."},{"key":"3231_CR13","doi-asserted-by":"publisher","unstructured":"Wu, F., Liu, W., Wang, S., et al.: Improved mine pedestrian detection algorithm based on YOLOv4-Tiny. In Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022). SPIE 12462, 644\u2013649 (2023). https:\/\/doi.org\/10.1117\/12.2661076","DOI":"10.1117\/12.2661076"},{"issue":"12","key":"3231_CR14","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.3390\/s22124331","volume":"22","author":"Z Xu","year":"2022","unstructured":"Xu, Z., Li, J., Meng, Y., et al.: Cap-yolo: channel attention based pruning yolo for coal mine real-time intelligent monitoring. Sensors 22(12), 4331 (2022). https:\/\/doi.org\/10.3390\/s22124331","journal-title":"Sensors"},{"key":"3231_CR15","doi-asserted-by":"publisher","first-page":"107195","DOI":"10.1016\/j.patcog.2020.107195","volume":"103","author":"X Wei","year":"2020","unstructured":"Wei, X., et al.: Pedestrian detection in underground mines via parallel feature transfer network. Pattern Recognit. 103, 107195 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107195","journal-title":"Pattern Recognit."},{"key":"3231_CR16","unstructured":"Wolf, T., et al.: Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations. (2020)"},{"key":"3231_CR17","doi-asserted-by":"publisher","unstructured":"Carion, N., et al.: End-to-end object detection with transformers. European conference on computer vision. Cham: Springer International Publishing, (2020).https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"3231_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, H., Li, F., Liu, S., et al.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605, (2022). https:\/\/doi.org\/10.48550\/arXiv.2203.03605","DOI":"10.48550\/arXiv.2203.03605"},{"key":"3231_CR19","doi-asserted-by":"publisher","unstructured":"Zhu, X., Su, W., Lu, L., et al.: Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159, (2020). https:\/\/doi.org\/10.48550\/arXiv.2010.04159","DOI":"10.48550\/arXiv.2010.04159"},{"key":"3231_CR20","doi-asserted-by":"publisher","unstructured":"Hou, L., Kwok, J.T.: Loss-aware weight quantization of deep networks. arXiv preprint arXiv:1802.08635, (2018). https:\/\/doi.org\/10.48550\/arXiv.1802.08635","DOI":"10.48550\/arXiv.1802.08635"},{"issue":"1","key":"3231_CR21","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3390\/ai3010011","volume":"3","author":"Q Huang","year":"2022","unstructured":"Huang, Q.: Weight-quantized squeezenet for resource-constrained robot vacuums for indoor obstacle classification. AI 3(1), 180\u2013193 (2022). https:\/\/doi.org\/10.3390\/ai3010011","journal-title":"AI"},{"key":"3231_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3141665","author":"Z Tang","year":"2022","unstructured":"Tang, Z., Luo, L., Xie, B., et al.: Automatic sparse connectivity learning for neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3141665","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3231_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3262952","author":"W Hu","year":"2023","unstructured":"Hu, W., Che, Z., Liu, N., et al.: Channel pruning via class-aware trace ratio optimization. IEEE Trans. Neural Netw. Learn. Syst. (2023). https:\/\/doi.org\/10.1109\/TNNLS.2023.3262952","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"3231_CR24","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.3390\/s23094294","volume":"23","author":"M Imam","year":"2023","unstructured":"Imam, M., Baina, K., Tabii, Y., et al.: The future of mine safety: a comprehensive review of anti-collision systems based on computer vision in underground mines. Sensors 23(9), 4294 (2023). https:\/\/doi.org\/10.3390\/s23094294","journal-title":"Sensors"},{"issue":"8","key":"3231_CR25","first-page":"2190","volume":"42","author":"MS Zhining","year":"2017","unstructured":"Zhining, M.S., Mei, L.: Mine non-uniform illumination video image enhancement algorithm based on illumination adjustment. J. Coal Sci. Technol. 42(8), 2190\u20132197 (2017)","journal-title":"J. Coal Sci. Technol."},{"key":"3231_CR26","doi-asserted-by":"publisher","unstructured":"Liu, X., Wu, Y., Liang, W., et al.: High resolution SAR image classification using global-local network structure based on vision transformer and CNN. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3151353","DOI":"10.1109\/LGRS.2022.3151353"},{"key":"3231_CR27","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S, et al.: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 770\u2013778. https:\/\/doi.org\/10.48550\/arXv.1512.03385","DOI":"10.48550\/arXv.1512.03385"},{"key":"3231_CR28","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pp. 1\u20139.https:\/\/doi.org\/10.48550\/arXiv.1409.4842","DOI":"10.48550\/arXiv.1409.4842"},{"key":"3231_CR29","doi-asserted-by":"publisher","unstructured":"Howard, A.G., Zhu, M., Chen, B., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, (2017).https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"3231_CR30","doi-asserted-by":"publisher","unstructured":"Han, K., Wang, Y., Tian, Q., et al.: Ghostnet: More features from cheap operations. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020, pp. 1580\u20131589. https:\/\/doi.org\/10.48550\/arXiv.1911.11907","DOI":"10.48550\/arXiv.1911.11907"},{"key":"3231_CR31","doi-asserted-by":"publisher","unstructured":"Hu, J., Li, S., Gang, S.: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. (2018).https:\/\/doi.org\/10.48550\/arXiv.1709.01507","DOI":"10.48550\/arXiv.1709.01507"},{"key":"3231_CR32","doi-asserted-by":"publisher","unstructured":"Woo, S., et al.: Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV). (2018).https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3231_CR33","unstructured":"Huang, H., et al.: Channel prior convolutional attention for medical image segmentation. arXiv preprint arXiv:2306.05196 (2023)"},{"key":"3231_CR34","doi-asserted-by":"publisher","unstructured":"Liu, X., et al.: EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.07027","DOI":"10.48550\/arXiv.2305.07027"},{"key":"3231_CR35","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: A hierarchical graph network for 3d object detection on point clouds. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. (2020)","DOI":"10.1109\/CVPR42600.2020.00047"},{"key":"3231_CR36","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"3231_CR37","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Alexey, B., Hong-Yuan, M.L.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. (2023). https:\/\/doi.org\/10.48550\/arXiv.2207.02696","DOI":"10.48550\/arXiv.2207.02696"},{"key":"3231_CR38","doi-asserted-by":"publisher","unstructured":"Reis, D., et al.: Real-Time Flying Object Detection with YOLOv8. arXiv preprint arXiv:2305.09972 (2023). https:\/\/doi.org\/10.48550\/arXv.2305.09972","DOI":"10.48550\/arXv.2305.09972"},{"key":"3231_CR39","unstructured":"Lv, W., Xu, S., Zhao, Y., et al.: Detrs beat yolos on real-time object detection. arXiv preprint arXiv:2304.08069, (2023)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03231-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03231-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03231-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T08:21:51Z","timestamp":1721031711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03231-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,4]]},"references-count":39,"journal-issue":{"issue":"6-7","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["3231"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03231-z","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,4]]},"assertion":[{"value":"22 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2024","order":4,"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"}},{"value":"There are no moral or ethical issues with the papers written by the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}