{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:46:59Z","timestamp":1774406819896,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52174154"],"award-info":[{"award-number":["52174154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52174154"],"award-info":[{"award-number":["52174154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52174154"],"award-info":[{"award-number":["52174154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52174154"],"award-info":[{"award-number":["52174154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01518-5","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T18:05:05Z","timestamp":1721757905000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Lightweight detection model for coal gangue identification based on improved YOLOv5s"],"prefix":"10.1007","volume":"21","author":[{"given":"Deyong","family":"Shang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zehua","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuntao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"issue":"01","key":"1518_CR1","first-page":"34","volume":"44","author":"G Wang","year":"2019","unstructured":"Wang, G., Zhao, G., Ren, H.: Analysis of key core technologies of smart coal mine and intelligent mining. Journal of Coal 44(01), 34\u201341 (2019)","journal-title":"Journal of Coal"},{"key":"1518_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.mineng.2018.12.004","volume":"132","author":"JT McCoy","year":"2019","unstructured":"McCoy, J.T., Auret, L.: Machine learning applications in minerals processing: A review. Miner. Eng. 132, 95\u2013109 (2019)","journal-title":"Miner. Eng."},{"issue":"07","key":"1518_CR3","first-page":"1847","volume":"41","author":"J Sun","year":"2016","unstructured":"Sun, J., Chen, B.: A method for coal rock identification based on statistical modeling in double-tree complex wavelet domain. Journal of Coal 41(07), 1847\u20131858 (2016)","journal-title":"Journal of Coal"},{"key":"1518_CR4","unstructured":"Li, Man, yon Duan, xiangan Cao, changyue Liu, Kaikai Sun, and Hao Liu.: Image identification method and system for coal gangue sorting robot. Journal of Coal 45(10), 3636\u20133644 (2020)"},{"issue":"3","key":"1518_CR5","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/min13030329","volume":"13","author":"G Xue","year":"2023","unstructured":"Xue, G., Hou, P., Li, S., Qian, X., Han, S., Gao, S.: Coal gangue recognition during coal preparation using an adaptive boosting algorithm. Minerals 13(3), 329 (2023)","journal-title":"Minerals"},{"issue":"11","key":"1518_CR6","first-page":"3051","volume":"43","author":"J Wang","year":"2018","unstructured":"Wang, J., Li, L., Yang, S.: Experimental study on the extraction of grayscale and texture features of coal gangue images under different illumination levels. Journal of Coal 43(11), 3051\u20133061 (2018)","journal-title":"Journal of Coal"},{"key":"1518_CR7","doi-asserted-by":"crossref","unstructured":"Hobson, David\u00a0M., Carter, Robert\u00a0M, Yan, Yong, Lv, Zhixin: Differentiation between coal and stone through image analysis of texture features. In 2007 IEEE International Workshop on Imaging Systems and Techniques, pages 1\u20134. IEEE","DOI":"10.1109\/IST.2007.379597"},{"issue":"08","key":"1518_CR8","first-page":"137","volume":"50","author":"L Hengrun","year":"2018","unstructured":"Hengrun, L., Wang, W., Zhiqiang, X., Lv, Z., Li, Q.: Research on feature extraction and classification of coal gangue based on machine vision. Coal Engineering 50(08), 137\u2013140 (2018)","journal-title":"Coal Engineering"},{"key":"1518_CR9","doi-asserted-by":"crossref","unstructured":"Yan, Pengcheng, Sun, Quansheng, Yin, Nini, Hua, Lili, Shang, Songhang, Zhang, Chaoyin: Detection of coal and gangue based on improved yolov5.1 which embedded scse module*. Measurement 188-):188, (2022)","DOI":"10.1016\/j.measurement.2021.110530"},{"issue":"3","key":"1518_CR10","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s11554-022-01215-1","volume":"19","author":"H Pan","year":"2022","unstructured":"Pan, H., Shi, Y., Lei, X., Wang, Z., Xin, F.: Fast identification model for coal and gangue based on the improved tiny yolo v3. J. Real-Time Image Proc. 19(3), 687\u2013701 (2022)","journal-title":"J. Real-Time Image Proc."},{"issue":"08","key":"1518_CR11","first-page":"157","volume":"54","author":"Xiufan Cai and jinchen Xie","year":"2022","unstructured":"Xiufan Cai and jinchen Xie: Study of yolov4 coal gangue detection method. Coal Engineering 54(08), 157\u2013162 (2022)","journal-title":"Coal Engineering"},{"issue":"24","key":"1518_CR12","first-page":"72","volume":"40","author":"W Lai","year":"2020","unstructured":"Lai, W., Zhou, M., Feng, H., Bian, K., Song, H.: Multi-spectral imaging and improved yolo v4 based gangue detection. J. Opt. 40(24), 72\u201380 (2020)","journal-title":"J. Opt."},{"issue":"06","key":"1518_CR13","first-page":"2207","volume":"45","author":"X Zhiqiang","year":"2020","unstructured":"Zhiqiang, X., Lv, Z., Wang, W., Zhang, K., Lv, H.: Machine vision identification method and optimization of coal gangue intelligent sorting. Journal of Coal 45(06), 2207\u20132216 (2020)","journal-title":"Journal of Coal"},{"issue":"3","key":"1518_CR14","first-page":"50","volume":"48","author":"S Lei","year":"2021","unstructured":"Lei, S., Xiao, X., Zhang, M.: Research on coal gangue identification method based on improved yolov3. Mining Safety and Environmental Protection 48(3), 50\u201355 (2021)","journal-title":"Mining Safety and Environmental Protection"},{"key":"1518_CR15","doi-asserted-by":"crossref","unstructured":"Gui, Fangjun, Yu, Shuo, Zhang, Hailan, Zhu, Hongda: Coal gangue recognition algorithm based on improved yolov5. In 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), volume\u00a02, pages 1136\u20131140. IEEE","DOI":"10.1109\/ICIBA52610.2021.9687869"},{"key":"1518_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100762","volume":"22","author":"G Xue","year":"2023","unstructured":"Xue, G., Li, S., Hou, P., Gao, S., Tan, R.: Research on lightweight yolo coal gangue detection algorithm based on resnet18 backbone feature network. Internet of Things 22, 100762 (2023)","journal-title":"Internet of Things"},{"key":"1518_CR17","doi-asserted-by":"crossref","unstructured":"ChunMing, Wu, Sun, YiQian, Wang, TiaoJun, Liu, YaLi: Underwater trash detection algorithm based on improved yolov5s. Journal of Real-Time Image Processing 19(5), 911\u2013920 1 (2022)","DOI":"10.1007\/s11554-022-01232-0"},{"issue":"02","key":"1518_CR18","first-page":"30","volume":"12","author":"L Long","year":"2023","unstructured":"Long, L., Cheng, H., Liang, H., Zhao, S., Liu, Z., Li, Z.: Real-time x-ray weld defect detection based on lightweight yolo network. Web New Media Technology 12(02), 30\u201338 (2023)","journal-title":"Web New Media Technology"},{"key":"1518_CR19","unstructured":"Glenn, J.: yolov5. available at https:\/\/github.com\/ultralytics\/yolov5 (accessed on 22 march 2023). (2020)"},{"issue":"5","key":"1518_CR20","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s11554-023-01347-y","volume":"20","author":"H Wang","year":"2023","unstructured":"Wang, H., Zhang, F., Wang, H., Li, Z., Wang, Y.: Real-time detection and location of reserved anchor hole in coal mine roadway support steel belt. J. Real-Time Image Proc. 20(5), 89 (2023)","journal-title":"J. Real-Time Image Proc."},{"key":"1518_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Shengying, Zhao, Jing, Xiaoye Zhao, Na\u00a0Ta, Wei, Haicheng: A real-time deep learning forest fire monitoring algorithm based on an improved pruned + kd model. Journal of Real-Time Image Processing, (1), (2021)","DOI":"10.1007\/s11554-021-01124-9"},{"key":"1518_CR22","unstructured":"Lin, Tsung-Yi, Doll\u00e1r, Piotr, Girshick, Ross, He, Kaiming: Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117\u20132125"},{"key":"1518_CR23","unstructured":"Shu Liu, Lu., Qi, Haifang Qin, Shi, Jianping, Jia, Jiaya: Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition pages 8759\u20138768"},{"key":"1518_CR24","unstructured":"Ma, Ningning, Zhang, Xiangyu, Zheng, Hai-Tao., Sun, Jian: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) pages 116\u2013131"},{"key":"1518_CR25","unstructured":"Ding, Xiaohan, Zhang, Xiangyu, Ma, Ningning, Han, Jungong, Ding, Guiguang, Sun, Jian: Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pages 13733\u201313742"},{"key":"1518_CR26","unstructured":"Simonyan, Karen, Zisserman, Andrew: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, (2014)"},{"key":"1518_CR27","unstructured":"Chollet, Fran\u00e7ois: Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251\u20131258"},{"key":"1518_CR28","unstructured":"Jiang, Borui, Luo, Ruixuan, Mao, Jiayuan, Xiao, Tete, Jiang, Yuning: Acquisition of localization confidence for accurate object detection. In Proceedings of the European conference on computer vision (ECCV) pages 784\u2013799"},{"key":"1518_CR29","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence 34, 12993\u201313000 (2020)","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"1518_CR30","unstructured":"Tong, Zanjia, Chen, Yuhang, Xu, Zewei, Yu, Rong: Wise-iou: Bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051, (2023)"},{"key":"1518_CR31","unstructured":"Jing Y.: fatigue dataset. available at https:\/\/aistudio.baidu.com\/datasetdetail\/106856 (accessed on 22 march 2023). (2021)"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01518-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01518-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01518-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T16:33:32Z","timestamp":1724776412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01518-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1518"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01518-5","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,23]]},"assertion":[{"value":"4 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"137"}}