{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:50:18Z","timestamp":1767707418874,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:00:00Z","timestamp":1753401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"multi-spectral visual fusion monitoring and early fire source location of external mine fires","award":["52364017","2016YFC0801800"],"award-info":[{"award-number":["52364017","2016YFC0801800"]}]},{"name":"research program of emergency treatment and rescue technology for serious accidents in coal mines","award":["52364017","2016YFC0801800"],"award-info":[{"award-number":["52364017","2016YFC0801800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50\u201395) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved.<\/jats:p>","DOI":"10.3390\/e27080791","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T14:40:02Z","timestamp":1753454402000},"page":"791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Fusion of Entropy-Enhanced Image Processing and Improved YOLOv8 for Smoke Recognition in Mine Fires"],"prefix":"10.3390","volume":"27","author":[{"given":"Xiaowei","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"ref_1","first-page":"313","article-title":"Research on coal-mine safe production conception","volume":"36","author":"Sun","year":"2011","journal-title":"J. China Coal Soc."},{"key":"ref_2","first-page":"3253","article-title":"Mine external fire recognition and anti-interference method based on the internal concavity of image","volume":"49","author":"Sun","year":"2024","journal-title":"J. China Coal Soc."},{"key":"ref_3","first-page":"1","article-title":"Research on ultraviolet image perception method of mine electric spark and thermal power disaster","volume":"48","author":"Sun","year":"2022","journal-title":"J. Mine Autom."},{"key":"ref_4","first-page":"591","article-title":"Decision Support System for the Prediction of Mine Fire Levels in Underground Coal Mining Using Machine Learning Approaches","volume":"39","author":"Kamran","year":"2022","journal-title":"Min. Metall. Explor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.ssci.2011.09.007","article-title":"Dynamic numerical simulation of coal mine fire for escape capsule installation","volume":"50","author":"Zhang","year":"2012","journal-title":"Saf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ijmst.2018.05.005","article-title":"Fire behaviour of multiple fires in a mine drift with longitudinal ventilation","volume":"29","author":"Hansen","year":"2019","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jia, J.Z., and Wang, F.X. (2022). Study on emergency escape route planning under fire accidents in the Burtai coal mine. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-15437-7"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lei, K.J., Qiu, D.D., Zhang, S.L., Wang, Z.C., and Jin, Y. (2023). Coal Mine Fire Emergency Rescue Capability Assessment and Emergency Disposal Research. Sustainability, 15.","DOI":"10.3390\/su15118501"},{"key":"ref_9","first-page":"4141236","article-title":"Modified Stochastic Petri Net-Based Modeling and Optimization of Emergency Rescue Processes during Coal Mine Accidents","volume":"2021","author":"Li","year":"2021","journal-title":"Geofluids"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.psep.2016.06.026","article-title":"Information fusion of plume control and personnel escape during the emergency rescue of external-caused fire in a coal mine","volume":"103","author":"Wang","year":"2016","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.psep.2021.03.010","article-title":"Model and simulation analysis of fire development and gas flowing influenced by fire zone sealing in coal mine","volume":"149","author":"Shi","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_12","first-page":"559","article-title":"Field Verification of an Improved Mine Fire Location Model","volume":"38","author":"Bahrami","year":"2021","journal-title":"Min. Metall. Explor."},{"key":"ref_13","first-page":"112","article-title":"Emergency rescue technology and equipment of mine extraordinary accidents","volume":"45","author":"Sun","year":"2017","journal-title":"Coal Sci. Technol."},{"key":"ref_14","unstructured":"National Mine Safety Administration (2025, April 01). The Case of \u201c9\u201324\u201d Major Fire Accident in Shanzhushu Mine of Guizhou Panjiang Fine Coal Co, Available online: https:\/\/www.chinamine-safety.gov.cn\/zfxxgk\/fdzdgknr\/sgcc\/sgalks\/202409\/t20240919_501797.shtml."},{"key":"ref_15","unstructured":"National Mine Safety Administration (2025, April 01). The case of \u201c5-9\u201d large fire accident in Gengcun coal mine of Henan Sanmenxia Henan Dayou Energy Co, Available online: https:\/\/www.chinamine-safety.gov.cn\/zfxxgk\/fdzdgknr\/sgcc\/sgalks\/202309\/t20230922_463749.shtml."},{"key":"ref_16","unstructured":"National Mine Safety Administration (2025, April 01). Chongqing Yongchuan District Hangshuidong Coal Industry Co, Available online: https:\/\/www.chinamine-safety.gov.cn\/zfxxgk\/fdzdgknr\/sgcc\/sgalks\/202107\/t20210721_392499.shtml."},{"key":"ref_17","unstructured":"National Mine Safety Administration (2025, April 01). The Case of \u201c9\u201327\u201d Major Fire Accident in Songzao Coal Mine of Chongqing Nengtou Yu New Energy Co, Available online: https:\/\/www.chinamine-safety.gov.cn\/zfxxgk\/fdzdgknr\/sgcc\/sgalks\/202107\/t20210723_392765.shtml."},{"key":"ref_18","first-page":"1","article-title":"Mine external fire sensing method","volume":"47","author":"Sun","year":"2021","journal-title":"J. Mine Autom."},{"key":"ref_19","first-page":"12","article-title":"Binocular vision-based perception and positioning method of mine external fire","volume":"47","author":"Sun","year":"2021","journal-title":"J. Mine Autom."},{"key":"ref_20","first-page":"1399","article-title":"Identifying the Location and Size of an Underground Mine Fire with Simulated Ventilation Data and Random Forest Model","volume":"40","author":"Xue","year":"2023","journal-title":"Min. Metall. Explor."},{"key":"ref_21","first-page":"17","article-title":"Smog detection based on texture features and optical flow vector of contour","volume":"35","author":"Zhang","year":"2016","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_22","first-page":"171","article-title":"Video Smoke Detection Based on HSV Color Space Feature","volume":"32","author":"Cao","year":"2022","journal-title":"Comput. Technol. Dev."},{"key":"ref_23","first-page":"291","article-title":"Fire Smoke Detection in Highway Tunnels Based on Video Images","volume":"42","author":"Deng","year":"2022","journal-title":"Tunn. Constr."},{"key":"ref_24","first-page":"526","article-title":"Convolutional Neural Network for Smoke Recognition Based on Feature Analysis","volume":"51","author":"Yin","year":"2021","journal-title":"Radio Eng."},{"key":"ref_25","first-page":"1230","article-title":"Smoke detection based on computer vision in coal mine","volume":"35","author":"Wang","year":"2016","journal-title":"J. Liaoning Tech. Univ. Nat. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1007\/s10694-022-01231-4","article-title":"Deep Convolutional Network with Pixel-Aware Attention for Smoke Recognition","volume":"58","author":"Cheng","year":"2022","journal-title":"Fire Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"121665","DOI":"10.1016\/j.eswa.2023.121665","article-title":"Fsdf: A High-Performance Fire Detection Framework","volume":"238","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Piao, Y., Wang, H., Zhang, H., and Li, B. (2024). An Improved Forest Smoke Detection Model Based on YOLOv8. Forests, 15.","DOI":"10.3390\/f15030409"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108219","DOI":"10.1016\/j.knosys.2022.108219","article-title":"Fast Forest Fire Smoke Detection Using Mvmnet","volume":"241","author":"Hu","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00138-018-0990-3","article-title":"Convolutional Neural Networks Based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition","volume":"30","author":"Yuan","year":"2019","journal-title":"Mach. Vis. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yin, H., Chen, M., Fan, W., Jin, Y., Hassan, S.G., and Liu, S. (2022). Efficient Smoke Detection Based on Yolo V5s. Mathematics, 10.","DOI":"10.3390\/math10193493"},{"key":"ref_32","first-page":"47","article-title":"Research on Fire Recognition Based on Smoke Image Dynamic Multi-frame Difference Method","volume":"36","author":"Yang","year":"2021","journal-title":"Autom. Instrum."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13911","DOI":"10.1007\/s00500-021-06333-9","article-title":"A remark on the maximum entropy principle in uncertainty theory","volume":"25","author":"Ma","year":"2021","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yu, X.C., and Li, X.W. (2023). Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet. Entropy, 25.","DOI":"10.3390\/e25030412"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., and Akin, B. (2024, January 17\u201321). MobileNetV4-Universal Models for the Mobile Ecosystem. Proceedings of the 2024 IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1007\/978-3-031-73661-2_5"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/8\/791\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:16:10Z","timestamp":1760033770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/8\/791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,25]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["e27080791"],"URL":"https:\/\/doi.org\/10.3390\/e27080791","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2025,7,25]]}}}