{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:49:31Z","timestamp":1785408571924,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52364017"],"award-info":[{"award-number":["52364017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["52364017"],"award-info":[{"award-number":["52364017"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Inner Mongolia Autonomous Region","award":["2025YFHH0001"],"award-info":[{"award-number":["2025YFHH0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To address the poor detection performance in low-light underground coal mine belt conveyors caused by information entropy degradation and high background noise, as well as the difficulty in multi-scale target extraction due to uneven entropy distribution, this paper proposes an efficient foreign object detection model named SIRI-YOLO based on an improved YOLOv11n architecture. First, a Self-Calibrating Illumination Network (SCINet) is introduced to restore image information entropy and enhance low-light adaptability. Second, the C2PSA module is enhanced to C2PSA-IRMB by incorporating an Inverted Residual Mobile Block (IRMB), improving multi-scale feature utilization and reducing ineffective entropy increase. Third, an improved Reparameterized Generalized Feature Pyramid Network (RepGFPN) is adopted to strengthen the fusion of high-level semantics and low-level spatial features, reducing information entropy loss during feature pyramid transfer. Finally, the Inner-MPDIoU loss function is introduced to replace CIoU, achieving more accurate entropy minimization from a KL divergence perspective. Experimental results on a dataset containing large coal chunks and anchor rods show that SIRI-YOLO achieves 92.8% mAP@50, 59.4% mAP@50:95, 89.5% precision, and 87.2% recall, with only 2.92M parameters and 70.01 FPS, outperforming mainstream YOLO models. Furthermore, on the public ExDark low-light dataset, SIRI-YOLO improves mAP@50 by 4.2% over YOLOv11n, demonstrating strong generalization across different low-light and complex scenarios. The proposed method effectively handles uneven illumination, scale variation, and complex backgrounds, offering a practical solution for coal mine safety through system entropy reduction.<\/jats:p>","DOI":"10.3390\/e28060673","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:06:52Z","timestamp":1781600812000},"page":"673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SIRI-YOLO: A Foreign Object Detection Method for Belt Conveyors in High-Entropy Underground Scenes"],"prefix":"10.3390","volume":"28","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rengang","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinping","family":"Xiao","sequence":"additional","affiliation":[{"name":"Beijing Aerospace Changfeng ScienceTechnology Industry Group Co., Ltd., Beijing 100854, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref_1","first-page":"424","article-title":"Progress and prospect of intelligent mining technology and equipment for open-pit coal mines","volume":"54","author":"Wang","year":"2026","journal-title":"Coal Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dai, Y., and Liu, W. (2023). GL-YOLO-Lite: A novel lightweight fallen person detection model. Entropy, 25.","DOI":"10.3390\/e25040587"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, S., Wang, X., Zheng, W., and Xu, Y. (2025). YOLO-GRBI: An Enhanced Lightweight Detector for Non-Cooperative Spatial Target in Complex Orbital Environments. Entropy, 27.","DOI":"10.3390\/e27090902"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108398","DOI":"10.1109\/ACCESS.2023.3321385","article-title":"DF-YOLO: Highly accurate transmission line foreign object detection algorithm","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s42452-025-08051-5","article-title":"A systematic review of deep learning methods for low-light image enhancement and object detection","volume":"8","author":"Singh","year":"2026","journal-title":"Discov. Appl. Sci."},{"key":"ref_6","first-page":"156","article-title":"PE-YOLO: Pyramid Enhancement Network for Dark Object Detection","volume":"Volume 14260","author":"Iliadis","year":"2023","journal-title":"Proceedings of the Artificial Neural Networks and Machine Learning\u2014ICANN 2023; Lecture Notes in Computer Science"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hashmi, K.A., Kallempudi, G., Stricker, D., and Afzal, M.Z. (2023). FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.00619"},{"key":"ref_8","first-page":"106","article-title":"Lightweight low-light object detection algorithm based on CDD-YOLO","volume":"61","author":"Shi","year":"2025","journal-title":"Comput. Eng. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1007\/s00371-023-03039-x","article-title":"SCDet: Decoupling discriminative representation for dark object detection via supervised contrastive learning","volume":"40","author":"Lin","year":"2024","journal-title":"Vis. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TIV.2020.3039456","article-title":"Night-to-day: Online image-to-image translation for object detection within autonomous driving by night","volume":"6","author":"Schutera","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, L., Cao, H., Zou, H., and Wu, H. (2025). Dmn-yolo: A robust yolov11 model for detecting apple leaf diseases in complex field conditions. Agriculture, 15.","DOI":"10.3390\/agriculture15111138"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, K., and Jiang, S. (2025). Forest fire detection algorithm based on improved YOLOv11n. Sensors, 25.","DOI":"10.3390\/s25102989"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Z., Guo, X., Li, C., and Teng, G. (2025). Wheat Head Detection in Field Environments Based on an Improved YOLOv11 Model. Agriculture, 15.","DOI":"10.3390\/agriculture15161765"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, L., Sun, T., Guo, X., and Yuan, Z. (2026). Multi-Objective Detection of River and Lake Spaces Based on YOLOv11n. Sensors, 26.","DOI":"10.3390\/s26041274"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, L., Cui, W., Li, J., Han, G., Zhou, Q., Lan, Y., Zhao, J., and Qiao, Y. (2025). A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n. Appl. Sci., 15.","DOI":"10.3390\/app15148085"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, J., Yue, Z., Hu, Y., Wang, M., and Zheng, B. (2024). Thermal Defect Detection in Power Equipment Based on YOLOv8s-SCINet-GAM Model with Low-Light Image Enhancement Algorithm. Proceedings of the 2024 4th International Conference on Energy, Power and Electrical Engineering (EPEE), Wuhan, China, September 2024, IEEE.","DOI":"10.1109\/EPEE63731.2024.10875389"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, S., Zhou, L., Liu, H., and Li, Z. (2025). Dark-Yolo: A low-light object detection algorithm integrating multiple attention mechanisms. Appl. Sci., 15.","DOI":"10.3390\/app15095170"},{"key":"ref_18","unstructured":"Huang, D., Chen, W., Pan, Y., and Chen, H. (2025). A New Target Detection Model in Complex Environments Based on Unmanned Aerial Vehicle Images. Proceedings of the Advanced Intelligent Computing Technology and Applications; Lecture Notes in Computer Science, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yu, Z. (2024). RT-DETR-iRMB: A Lightweight Real-Time Small Object Detection Method. Proceedings of the 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, May 2024, IEEE.","DOI":"10.1109\/IMCEC59810.2024.10575494"},{"key":"ref_20","first-page":"354","article-title":"Transmission Line Equipment Defect Detection Based on Improved YOLO Network","volume":"Volume 2183","author":"Zhang","year":"2024","journal-title":"Proceedings of the Neural Computing for Advanced Applications\u20145th International Conference, NCAA 2024, Guilin, China, 5\u20137 July 2024, Proceedings, Part III"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8600608","DOI":"10.1109\/JPHOT.2024.3453943","article-title":"A Hybrid Attention Mechanism and RepGFPN Method for Detecting Wall Cracks in High-Altitude Cleaning Robots","volume":"16","author":"Liu","year":"2024","journal-title":"IEEE Photonics J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mei, M., Zhou, Z., Liu, W., and Ye, Z. (2024). GOI-YOLOv8 grouping offset and isolated GiraffeDet low-light target detection. Sensors, 24.","DOI":"10.3390\/s24175787"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, G., Qi, H., and Lv, K. (2025). Dgyolov8: An enhanced model for steel surface defect detection based on yolov8. Mathematics, 13.","DOI":"10.3390\/math13050831"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ding, Y., Jiang, C., Song, L., Liu, F., and Tao, Y. (2024). RVDR-YOLOv8: A weed target detection model based on improved YOLOv8. Electronics, 13.","DOI":"10.3390\/electronics13112182"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"22430","DOI":"10.1109\/ACCESS.2021.3137638","article-title":"T-YOLO: Tiny vehicle detection based on YOLO and multi-scale convolutional neural networks","volume":"11","author":"Carrasco","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104703","DOI":"10.1016\/j.infrared.2023.104703","article-title":"YOLO-CIR: The network based on YOLO and ConvNeXt for infrared object detection","volume":"131","author":"Zhou","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"83535","DOI":"10.1007\/s11042-024-18872-y","article-title":"Yolo-based object detection models: A review and its applications","volume":"83","author":"Vijayakumar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Q., Qian, Y., Hu, Y., Xue, Y., and Wang, H. (2023). Dp-yolo: Effective improvement based on yolo detector. Appl. Sci., 13.","DOI":"10.3390\/app132111676"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/978-3-030-44534-8_25","article-title":"A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny","volume":"Volume 12083","author":"Rincon","year":"2020","journal-title":"Proceedings of the Applied Reconfigurable Computing. Architectures, Tools, and Applications; Lecture Notes in Computer Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"119108","DOI":"10.1016\/j.eswa.2022.119108","article-title":"Real-time vehicle detection algorithm based on a lightweight You-Only-Look-Once (YOLOv5n-L) approach","volume":"213","author":"Bie","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, X., Liu, Y., Lu, D., Liang, B., and Tang, Y. (2023). Real-time defect detection for metal components: A fusion of enhanced Canny\u2013Devernay and YOLOv6 algorithms. Appl. Sci., 13.","DOI":"10.3390\/app13126898"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"122212","DOI":"10.1016\/j.eswa.2023.122212","article-title":"E-YOLO: Recognition of estrus cow based on improved YOLOv8n model","volume":"238","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shi, Y., Duan, Z., Qing, S., Zhao, L., Wang, F., and Yuwen, X. (2024). YOLOV9S-Pear: A lightweight YOLOV9S-Based improved model for young Red Pear Small-Target recognition. Agronomy, 14.","DOI":"10.3390\/agronomy14092086"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, Y., Liu, Z., Zhao, H., Tang, C., Liu, B., Li, Z., Wan, F., Qian, W., and Qiao, X. (2025). YOLO-YSTs: An improved YOLOv10n-based method for real-time field pest detection. Agronomy, 15.","DOI":"10.3390\/agronomy15030575"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Luo, R., Zhao, R., Ding, X., Peng, S., and Cai, F. (2025). High-precision complex orchard passion fruit detection using the phd-yolo model improved from yolov11n. Horticulturae, 11.","DOI":"10.3390\/horticulturae11070785"},{"key":"ref_36","unstructured":"Jun, E.L.T., Tham, M.L., and Kwan, B.H. (2024). A Comparative Analysis of RT-DETR and YOLOv8 for Urban Zone Aerial Object Detection. Proceedings of the 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Shah Alam, Malaysia, June 2024, IEEE."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guan, B., Wu, Y., Zhu, J., Kong, J., and Dong, W. (2025). GC-Faster RCNN: The object detection algorithm for agricultural pests based on improved hybrid attention mechanism. Plants, 14.","DOI":"10.3390\/plants14071106"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"125773","DOI":"10.1016\/j.eswa.2024.125773","article-title":"Multi-object garbage image detection algorithm based on SP-SSD","volume":"263","author":"Tan","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_39","first-page":"151","article-title":"Foreign object detection for underground coal mine conveyor belts in occlusion scenarios based on SDGW-YOLOv11","volume":"48","author":"Shaokai","year":"2025","journal-title":"Electr. Meas. Technol."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/673\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:23:38Z","timestamp":1781601818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/6\/673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["e28060673"],"URL":"https:\/\/doi.org\/10.3390\/e28060673","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]}}}