{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T11:26:50Z","timestamp":1778412410161,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Anhui\u00a0University\u00a0of\u00a0Science\u00a0and\u00a0Technology\u00a0Graduate\u00a0Innovation\u00a0Fund","award":["2024cx2075"],"award-info":[{"award-number":["2024cx2075"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11554-025-01691-1","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T12:39:55Z","timestamp":1747399195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multispectral imaging and enhanced YOLOv10n for efficient coal gangue detection in complex mining environments"],"prefix":"10.1007","volume":"22","author":[{"given":"Pengcheng","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pinghong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"1691_CR1","first-page":"102","volume":"23","author":"C Xu","year":"2010","unstructured":"Xu, C., Kang, X.: Hazards of coal gangue and its comprehensive utilization. Environ. Sci. Technol. 23, 102\u2013104 (2010)","journal-title":"Environ. Sci. Technol."},{"issue":"5","key":"1691_CR2","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3969\/j.issn.1001-3881.2021.05.011","volume":"49","author":"M Zhao","year":"2021","unstructured":"Zhao, M., Xuan, P., Zhang, S.: Structural design and analysis of parallel coal gangue sorting robot. Mach. Tool Hydraulics 49(5), 55\u201359 (2021). https:\/\/doi.org\/10.3969\/j.issn.1001-3881.2021.05.011","journal-title":"Mach. Tool Hydraulics"},{"key":"1691_CR3","unstructured":"Qianyan Cloud (Chongqing) Technology Co., Ltd. Coal selection robot and method based on image recognition: CN115007474A. 2022\u201309\u201306. https:\/\/wwwv3.cqvip.com\/doc\/patent\/2005542597. Accessed 10 July 2024"},{"key":"1691_CR4","unstructured":"Guizhou University, Guizhou Panjiang Anthracite Coal Co., Ltd. Coal gangue separation equipment and method based on combined detection of X-rays and ultrasound: CN117225731A. 2023\u201312\u201315. https:\/\/wwwv3.cqvip.com\/doc\/patent\/3334848427. Accessed 10 July 2024"},{"issue":"22","key":"1691_CR5","first-page":"272","volume":"38","author":"Y Shengwei","year":"2019","unstructured":"Shengwei, Y., Zugen, L.: Experimental study on the flotation recovery of carbon from coal gangue. Value Eng. 38(22), 272\u2013327 (2019)","journal-title":"Value Eng."},{"issue":"1","key":"1691_CR6","doi-asserted-by":"publisher","first-page":"015404","DOI":"10.1088\/1361-6501\/acfab1","volume":"35","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Li, D., Guo, Y., Wang, S., Zhao, D., Chen, W., Zhang, H.: Research on coal gangue recognition method based on XBS-YOLOv5s. Meas. Sci. Technol. 35(1), 015404 (2024)","journal-title":"Meas. Sci. Technol."},{"issue":"3","key":"1691_CR7","first-page":"329","volume":"13","author":"X Guanghui","year":"2023","unstructured":"Guanghui, X., Peng, H., Sanxi, L., Xiaoling, Q., Sicong, H., Song, G.: Coal gangue recognition during coal preparation using an adaptive boosting algorithm. Minerals 13(3), 329 (2023)","journal-title":"Minerals"},{"issue":"1","key":"1691_CR8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1080\/19392699.2021.2002852","volume":"43","author":"J Jiang","year":"2023","unstructured":"Jiang, J., Han, Y., Zhao, H., Suo, J., Cao, Q.: Recognition and sorting of coal and gangue based on image process and multilayer perceptron. Int. J. Coal Prepar. Util. 43(1), 54\u201372 (2023)","journal-title":"Int. J. Coal Prepar. Util."},{"issue":"2","key":"1691_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0281397","volume":"18","author":"L Zhao","year":"2023","unstructured":"Zhao, L., Han, L., Zhang, H., Liu, Z., Gao, F., Yang, S., Wang, Y.: Study on recognition of coal and gangue based on multimode feature and image fusion. PLoS ONE 18(2), e0281397 (2023)","journal-title":"PLoS ONE"},{"issue":"6","key":"1691_CR10","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1007\/s11554-023-01365-w","volume":"20","author":"G Ruxin","year":"2023","unstructured":"Ruxin, G., Yabo, D., Tengfei, W.: Research on coal gangue classification recognition method based on the combination of CNN and SVM. J. Real-Time Image Process. 20(6), 110 (2023)","journal-title":"J. Real-Time Image Process."},{"issue":"3","key":"1691_CR11","first-page":"287","volume":"53","author":"W Yuanzhe","year":"2024","unstructured":"Yuanzhe, W., Tengfei, L., Yuqiao, Z., Yi, J., Yidong, Li.: A review of multispectral target detection. Inf. Control. 53(3), 287\u2013301 (2024)","journal-title":"Inf. Control."},{"key":"1691_CR12","first-page":"402","volume":"6","author":"R Lin","year":"2024","unstructured":"Lin, R., Wei, L., Changhong, L., Lei, Z.: Non-destructive detection of aspergillus flavus in corn based on multispnectral imaging technology. J. Chin. Food Sci. 6, 402\u2013409 (2024)","journal-title":"J. Chin. Food Sci."},{"issue":"11","key":"1691_CR13","first-page":"3644","volume":"43","author":"W Wesong","year":"2023","unstructured":"Wesong, W., Chenxi, P., Bin, Y., Zhixin, W., Kejie, Q., Ying, W.: Research on measurement methods for flame temperature and emissivity distribution based on multispectral imaging technology. Spectrosc. Spect. Anal. 43(11), 3644\u20133652 (2023)","journal-title":"Spectrosc. Spect. Anal."},{"issue":"10","key":"1691_CR14","first-page":"1166","volume":"48","author":"Yu Zhang Feng","year":"2023","unstructured":"Zhang Feng, Yu., Xujun, D.L., Haili, Y., Jingyi, Z., Yanting, W.: Preliminary exploration of multispectral photoacoustic tomography technology in imaging testes of rats with varicocele. J. Chongqing Med. Univ. 48(10), 1166\u20131172 (2023)","journal-title":"J. Chongqing Med. Univ."},{"key":"1691_CR15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3788\/AOS202040.2411001","volume":"40","author":"L Wenhao","year":"2020","unstructured":"Wenhao, L., Mengran, Z., Feng, H., et al.: Coal gangue detection based on multispectral imaging and improved YOLOv4. Acta Optica Sinica 40, 66\u201374 (2020). https:\/\/doi.org\/10.3788\/AOS202040.2411001","journal-title":"Acta Optica Sinica"},{"issue":"5","key":"1691_CR16","first-page":"1","volume":"40","author":"Z Mengran","year":"2023","unstructured":"Mengran, Z., Sheng, L., Wenhao, L., Kai, B., Ziwei, Z., Ruhuan, S.: Multispectral recognition of coal gangue based on slime mold optimization extreme learning machine. J. Chongqing Technol. Business Univ. 40(5), 1\u20137 (2023)","journal-title":"J. Chongqing Technol. Business Univ."},{"issue":"01","key":"1691_CR17","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1111\/sjos.12424","volume":"47","author":"C Bouveyron","year":"2020","unstructured":"Bouveyron, C., Latouche, P., Mattei, P.A.: Exact dimensionality selection for Bayesian PCA. Scand. J. Stat. 47(01), 196\u2013211 (2020). https:\/\/doi.org\/10.1111\/sjos.12424","journal-title":"Scand. J. Stat."},{"issue":"3","key":"1691_CR18","doi-asserted-by":"publisher","first-page":"607","DOI":"10.16383\/j.aas.c230316","volume":"50","author":"Wu Yang Xusheng","year":"2024","unstructured":"Yang Xusheng, Wu., Fo, J.H., et al.: Multi-view human pose estimation based on progressive Gaussian filtering fusion. Acta Autom. Sinica 50(3), 607\u2013616 (2024). https:\/\/doi.org\/10.16383\/j.aas.c230316","journal-title":"Acta Autom. Sinica"},{"key":"1691_CR19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2405.14458","author":"A Wang","year":"2024","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: YOLOv10: real-time end-to-end object detection. arXiv (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.14458","journal-title":"arXiv"},{"key":"1691_CR20","doi-asserted-by":"publisher","DOI":"10.4850\/arXiv.2404.10518","author":"D Qin","year":"2024","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., Aggarwal, V., Zhu, T., Moro, D., Howard, A.: MobileNetV4\u2014universal models for the mobile ecosystem. arXiv (2024). https:\/\/doi.org\/10.4850\/arXiv.2404.10518","journal-title":"arXiv"},{"key":"1691_CR21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1807.065211","author":"W Sanghyun","year":"2018","unstructured":"Sanghyun, W., Lee, J., Young, J., et al.: CBAM: convolutional block attention module. arXiv (2018). https:\/\/doi.org\/10.48550\/arXiv.1807.065211","journal-title":"arXiv"},{"key":"1691_CR22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2112.05561","author":"L Yichao","year":"2021","unstructured":"Yichao, L., Zongru, S., Nico, H.: Global attention mechanism: retain information to enhance channel-spatial interactions. arXiv (2021). https:\/\/doi.org\/10.48550\/arXiv.2112.05561","journal-title":"arXiv"},{"key":"1691_CR23","volume-title":"Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2022, Grenoble, France, September 19\u201323, 2022, Proceedings, Part III","author":"R Sunkara","year":"2022","unstructured":"Sunkara, R., Luo, T.: No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. In: Amini, M.R., Canu, S., Fischer, A., Guns, T., Novak, P.P., Tsoumakas, G. (eds.) Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2022, Grenoble, France, September 19\u201323, 2022, Proceedings, Part III. Springer, Cham (2022)"},{"key":"1691_CR24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1905.09646","author":"X Li","year":"2019","unstructured":"Li, X., Hu, X., Yang, J.: Spatial group-wise enhance: improving semantic feature learning in convolutional networks. arXiv (2019). https:\/\/doi.org\/10.48550\/arXiv.1905.09646","journal-title":"arXiv"},{"issue":"1","key":"1691_CR25","first-page":"182","volume":"37","author":"Z-H Zhao","year":"2024","unstructured":"Zhao, Z.-H., Li, C.-X., Yang, S.-P.: GhostConv lightweight network design and research on fault diagnosis. J. Vib. Eng. 37(1), 182\u2013190 (2024)","journal-title":"J. Vib. Eng."},{"issue":"09","key":"1691_CR26","doi-asserted-by":"publisher","first-page":"10430","DOI":"10.1007\/s10489-021-02798-1","volume":"52","author":"C Yan","year":"2022","unstructured":"Yan, C., Zhang, H., Li, X., et al.: R-SSD: refined single shot multibox detector for pedestrian detection. Appl. Intell. 52(09), 10430\u201310447 (2022). https:\/\/doi.org\/10.1007\/s10489-021-02798-1","journal-title":"Appl. Intell."},{"key":"1691_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/s23177390","author":"Q Yang","year":"2023","unstructured":"Yang, Q., Ma, S., Guo, D., et al.: A small object detection method for oil leakage defects in substations based on improved faster-RCNN. Sensors (2023). https:\/\/doi.org\/10.3390\/s23177390","journal-title":"Sensors"},{"issue":"03","key":"1691_CR28","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1007\/s11119-020-09754-y","volume":"22","author":"L Fu","year":"2021","unstructured":"Fu, L., Feng, Y., Wu, J., et al.: Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model. Precis. Agric. 22(03), 754\u2013776 (2021). https:\/\/doi.org\/10.1007\/s11119-020-09754-y","journal-title":"Precis. Agric."},{"key":"1691_CR29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934","author":"A Bochkovskiy","year":"2020","unstructured":"Bochkovskiy, A., Wang, C., Yao, L., Hong, Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv (2020). https:\/\/doi.org\/10.48550\/arXiv.2004.10934","journal-title":"arXiv"},{"key":"1691_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10141711","author":"Y Jia","year":"2021","unstructured":"Jia, Y., Jiaming, Q., Jie, Z., et al.: A real-time detection algorithm for kiwifruit defects based on YOLOv5. Electronics (2021). https:\/\/doi.org\/10.3390\/electronics10141711","journal-title":"Electronics"},{"key":"1691_CR31","doi-asserted-by":"publisher","DOI":"10.4855\/arXiv.2207.02696","author":"W Chien-Yao","year":"2023","unstructured":"Chien-Yao, W., Alexey, B., Hong-Yuan, M.L.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv (2023). https:\/\/doi.org\/10.4855\/arXiv.2207.02696","journal-title":"arXiv"},{"key":"1691_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-54540-9","author":"W Xuewei","year":"2024","unstructured":"Xuewei, W., Jun, L.: Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment. Sci. Rep. (2024). https:\/\/doi.org\/10.1038\/s41598-024-54540-9","journal-title":"Sci. Rep."},{"key":"1691_CR33","doi-asserted-by":"publisher","DOI":"10.4550\/arXiv.2402.13616","author":"C Wang","year":"2024","unstructured":"Wang, C., Yao, Y., Hau, I., Hong, L., Mark, Y.: YOLOv9: learning what you want to learn using programmable gradient information. arXiv (2024). https:\/\/doi.org\/10.4550\/arXiv.2402.13616","journal-title":"arXiv"},{"key":"1691_CR34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.17725","author":"R Khanam","year":"2024","unstructured":"Khanam, R., Hussain, M.: YOLOv11: an overview of the key architectural enhancements. arXiv (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.17725","journal-title":"arXiv"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01691-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01691-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01691-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T16:53:53Z","timestamp":1751388833000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01691-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1691"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01691-1","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"3 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2025","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"111"}}