{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:45:58Z","timestamp":1788587158208,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Shanxi Province","award":["202501100101007"],"award-info":[{"award-number":["202501100101007"]}]},{"name":"Science and Technology Innovation Teams of Shanxi Province","award":["202304051001008"],"award-info":[{"award-number":["202304051001008"]}]},{"name":"Key Research and Development Project of Xinjiang Uygur Autonomous Department-Local Linkage Project","award":["2025B04038"],"award-info":[{"award-number":["2025B04038"]}]},{"name":"Independent Research Project of Xinjiang Intelligent Equipment Research Institute","award":["XJYJY2024002"],"award-info":[{"award-number":["XJYJY2024002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Video AI recognition is crucial for coal mine safety, but complex environments often yield low-quality images, hindering intelligent monitoring. Existing enhancement methods typically focus on image quality alone, lacking adaptability to specific tasks. Therefore, we propose Mine-DCE-YDT: a task-aware low-light image enhancement model that jointly optimizes enhancement with downstream object detection, ensuring enhanced images are both visually clearer and more conducive to accurate detection. Firstly, an improved Zero-DCE algorithm (Mine-DCE) is presented by introducing a Brightness-aware Mask Coordinate Attention (BMCA) module to improve illumination balance in the Value channel of the HSV image and a Multi-scale Detail Enhancement (MDE) module to reinforce textures and suppress noise. Then, Mine-DCE is co-modeled with YOLOv11n by training end-to-end via a joint loss fusing detection and enhancement quality losses to form Mine-DCE-YDT, which can enhance specific details containing image detection targets. Experimental results show that compared with Zero-DCE, Mine-DCE-YDT achieves reductions of 9.5% in NIQE and 35.5% in BRISQUE on the custom-constructed MineDataset and exhibits great enhancement performance on the public dataset LOL-V1. For the miner detection task in MineDataset, the integration of Mine-DCE-YDT with YOLOv11n achieves increases of 2.8% and 8.3% in mAP@0.5 and mAP@0.5:0.95, demonstrating its effectiveness in enhancing task-critical image features.<\/jats:p>","DOI":"10.3390\/s26061886","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T12:42:16Z","timestamp":1773751336000},"page":"1886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Task-Aware Low-Light Image Enhancement Method for Underground Coal Mine Monitoring"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4154-5321","authenticated-orcid":false,"given":"Zhirui","family":"Yan","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Xinjiang Intelligent Equipment Research Institute, Aksu 843000, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaru","family":"Li","sequence":"additional","affiliation":[{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Xinjiang Intelligent Equipment Research Institute, Aksu 843000, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Jin","sequence":"additional","affiliation":[{"name":"Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Xinjiang Intelligent Equipment Research Institute, Aksu 843000, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Tao","sequence":"additional","affiliation":[{"name":"Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Xinjiang Intelligent Equipment Research Institute, Aksu 843000, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5360-7007","authenticated-orcid":false,"given":"Yide","family":"Geng","sequence":"additional","affiliation":[{"name":"Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Xinjiang Intelligent Equipment Research Institute, Aksu 843000, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Solarz, J., Gawlik-Kobyli\u0144ska, M., Ostant, W., and Maciejewski, P. (2022). Trends in Energy Security Education with a Focus on Renewable and Nonrenewable Sources. Energies, 15.","DOI":"10.3390\/en15041351"},{"key":"ref_2","first-page":"349","article-title":"Review on key technologies of AI recognition for videos in coal mine","volume":"51","author":"Cheng","year":"2023","journal-title":"Coal Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, P., Liu, Y., Liang, Y., and Cui, D. (2025). Application of Artificial Intelligence in Predicting Coal Mine Disaster Risks: A Review. Sensors, 25.","DOI":"10.3390\/s25216586"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.psep.2021.01.046","article-title":"Evolutionary model of coal mine safety system based on multi-agent modeling","volume":"147","author":"Cheng","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84535","DOI":"10.1109\/ACCESS.2022.3197629","article-title":"Low-Light Image Enhancement: A Comparative Review and Prospects","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131052","DOI":"10.1016\/j.neucom.2025.131052","article-title":"A review of advancements in low-light image enhancement using deep learning","volume":"652","author":"Liu","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023, January 2\u20136). Segment anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_8","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Fernandez, P., Haziza, D., and Massa, F. (2023). Dinov2: Learning robust visual features without supervision. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TCE.2007.4429280","article-title":"Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement","volume":"53","author":"Ibrahim","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117435","DOI":"10.1016\/j.image.2025.117435","article-title":"A comprehensive review of low-light image enhancement methods","volume":"140","author":"Zheng","year":"2026","journal-title":"Signal Process. Image Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"0210002","DOI":"10.3788\/gzxb20204902.0210002","article-title":"Low-light Image Enhancement Method Using Retinex Method Based on YCbCr Color Space","volume":"49","author":"Tian","year":"2020","journal-title":"Acta Photon-Sin."},{"key":"ref_12","first-page":"4248","article-title":"Fuzzy enhancement algorithm of coal mine degradation image based on adaptive wavelet transform","volume":"45","author":"Fan","year":"2020","journal-title":"J. China Coal Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104223","DOI":"10.1016\/j.jvcir.2024.104223","article-title":"Advancements in low light image enhancement techniques and recent applications","volume":"103","author":"Anoop","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","article-title":"LLNet: A deep autoencoder approach to natural low-light image enhancement","volume":"61","author":"Lore","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., and Jiang, J. (2022). URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00581"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement Without Paired Supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"168342","DOI":"10.1109\/ACCESS.2021.3137993","article-title":"IceNet for Interactive Contrast Enhancement","volume":"9","author":"Ko","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9095","DOI":"10.1109\/TPAMI.2025.3586712","article-title":"Learning With Self-Calibrator for Fast and Robust Low-Light Image Enhancement","volume":"47","author":"Ma","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4019","DOI":"10.1007\/s40747-024-01387-2","article-title":"A low-light image enhancement method for personnel safety monitoring in underground coal mines","volume":"10","author":"Yang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_21","first-page":"222","article-title":"LMIENet enhanced object detection method for low light environment inunderground mines","volume":"52","author":"Tian","year":"2024","journal-title":"Coal Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"27897","DOI":"10.1038\/s41598-025-10779-4","article-title":"Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines","volume":"15","author":"Zhao","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","unstructured":"Liang, D., Li, L., Wei, M., Yang, S., Zhang, L., Yang, W., Du, Y., and Zhou, H. (March, January 22). Semantically Contrastive Learning for Low-Light Image Enhancement. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aej.2025.10.023","article-title":"Robust visual-based tracking using deep learning with image enhancement for reach stackers in container terminals","volume":"132","author":"An","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104063","DOI":"10.1016\/j.cviu.2024.104063","article-title":"Self-supervised network for low-light traffic image enhancement based on deep noise and artifacts removal","volume":"246","author":"Zhang","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103915","DOI":"10.1016\/j.cviu.2023.103915","article-title":"Joint learning of foreground, background and edge for salient object detection","volume":"240","author":"Wu","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, X., Zhang, M., and Luo, J. (2022). Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior. Sensors, 22.","DOI":"10.3390\/s22155593"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105671","DOI":"10.1016\/j.dsp.2025.105671","article-title":"Underwater image enhancement by combining wavelet transform and multiscale transformer networks","volume":"168","author":"Li","year":"2026","journal-title":"Digit. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.1109\/TIP.2022.3174398","article-title":"Toward Top-Down Just Noticeable Difference Estimation of Natural Images","volume":"31","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104589","DOI":"10.1016\/j.cviu.2025.104589","article-title":"Pay more attention to dark regions for faster shadow detection","volume":"263","author":"Wu","year":"2026","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Wu, Y., Tian, F., Feng, Z., and Li, Y. (2025). MSF-ACA: Low-Light Image Enhancement Network Based on Multi-Scale Feature Fusion and Adaptive Contrast Adjustment. Sensors, 25.","DOI":"10.20944\/preprints202506.0758.v1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104431","DOI":"10.1016\/j.cviu.2025.104431","article-title":"An effective CNN and Transformer fusion network for camouflaged object detection","volume":"259","author":"Zhang","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41597-025-05118-1","article-title":"An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines","volume":"12","author":"Zhao","year":"2025","journal-title":"Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dai, L., Qi, P., Lu, H., Liu, X., Hua, D., and Guo, X. (2023). Image Enhancement Method in Underground Coal Mines Based on an Improved Particle Swarm Optimization Algorithm. Appl. Sci., 13.","DOI":"10.3390\/app13053254"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1886\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:47:51Z","timestamp":1774000071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26061886"],"URL":"https:\/\/doi.org\/10.3390\/s26061886","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]}}}