{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:09:03Z","timestamp":1788786543119,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Adaptation and Transformation of Application System for the Information Technology Application Project of China Geological Survey","award":["GC-FYAD250419"],"award-info":[{"award-number":["GC-FYAD250419"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reliable human target detection in smoke-filled environments is essential for firefighting robots and rescue perception systems. However, conventional RGB cameras are severely degraded by dense smoke, while a single infrared or thermal imaging sensor cannot simultaneously provide sufficient structural details and reliable target-related thermal information. To address these challenges, this paper proposes a local variance-guided adaptive infrared\u2013thermal sensor fusion framework for human target detection in smoke-filled environments, aiming to alleviate smoke-induced degradation in multimodal perception through improved infrared representation and adaptive cross-modal information utilization. An improved dark channel prior-based infrared desmoking algorithm is designed, where guided filtering is employed to refine the transmission map, suppress halo artifacts, and enhance infrared image quality. Furthermore, a local variance-guided adaptive fusion strategy is proposed, which utilizes local variance as an information saliency metric to generate pixel-level adaptive modality weights for fusing desmoked infrared and thermal images. In addition, a lightweight YOLO11n detector is adopted to achieve efficient human target recognition while maintaining a favorable balance among detection accuracy, computational cost, and inference efficiency. Experimental results on the self-built dense-smoke dual-modal dataset demonstrate that the proposed framework achieves high detection performance with low model complexity and efficient detector-stage inference. The ablation results demonstrate the contribution of infrared\u2013thermal multimodal fusion to reliable smoke perception and indicate that the proposed local variance-guided adaptive fusion strategy maintains comparable detection accuracy while providing a better detector-stage speed\u2013accuracy balance than fixed-weight fusion. With a low parameter count, the adopted YOLO11n detector shows potential for future deployment on resource-constrained firefighting robotic platforms.<\/jats:p>","DOI":"10.3390\/s26175670","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:12:25Z","timestamp":1788783145000},"page":"5670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Local Variance-Guided Adaptive Infrared\u2013Thermal Sensor Fusion Framework for Human Target Detection in Smoke-Filled Firefighting Environments"],"prefix":"10.3390","volume":"26","author":[{"given":"Changyuan","family":"Shen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9964-5498","authenticated-orcid":false,"given":"Mingguang","family":"Diao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Liyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Longzhou","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Yongkang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Wenji","family":"Li","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rahman, F.S., Tannous, W.K., Avsar, G., Agho, K.E., Ghassempour, N., and Harvey, L.A. (2023). Economic costs of residential fires: A systematic review. Fire, 6.","DOI":"10.3390\/fire6100399"},{"key":"ref_2","first-page":"1","article-title":"Occupational hazards in firefighting: Systematic literature review","volume":"14","year":"2023","journal-title":"Saf. Health Work."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17298806231175238","DOI":"10.1177\/17298806231175238","article-title":"A sensor review for human detection with robotic systems in regular and smoky environments","volume":"20","author":"Gelfert","year":"2023","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Khalid, H., Sajid, S.M., Cheema, M.I., and Leitgeb, E. (2024). Optical signal attenuation through smog in controlled laboratory conditions. Photonics, 11.","DOI":"10.3390\/photonics11020172"},{"key":"ref_5","first-page":"22","article-title":"State of the art of smoke and fire detection using image processing","volume":"10","author":"Umar","year":"2017","journal-title":"Int. J. Signal Imaging Syst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tsai, P.F., Liao, C.H., and Yuan, S.M. (2022). Using deep learning with thermal imaging for human detection in heavy smoke scenarios. Sensors, 22.","DOI":"10.3390\/s22145351"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Truong, N.N., Le, M.H., Do, T.D., Tran, L.A., Nguyen, T.D., and Trinh, H.H. (2023). Efficient Infrared and Thermal Imaging Fusion Approach for Real-time Human Detection in Heavy Smoke Scenarios. Proceedings of the 2023 International Conference on System Science and Engineering (ICSSE), IEEE.","DOI":"10.1109\/ICSSE58758.2023.10227078"},{"key":"ref_8","first-page":"60","article-title":"VIF-YOLO: A Visible-Infrared Fusion YOLO Model for Real-Time Human Detection in Dense Smoke Environments","volume":"87","author":"Chen","year":"2026","journal-title":"Comput. Mater. Contin."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fotiou, D., Mygdalis, V., and Pitas, I. (Pattern Recognit. Lett., 2026). RoboFireFuseNet: Robust Fusion of Visible and Infrared Wildfire Imaging for Real-Time Flame and Smoke Segmentation, Pattern Recognit. Lett., in press.","DOI":"10.36227\/techrxiv.174673045.59969225\/v2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10694-024-01623-8","article-title":"An Image Restoration Method for Improving Matching Robustness of Indoor Smoke Scene","volume":"61","author":"Liang","year":"2025","journal-title":"Fire Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TETCI.2022.3173443","article-title":"Trends and prospects of techniques for haze removal from degraded images: A survey","volume":"6","author":"Sahu","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: A survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360671","article-title":"Single image dehazing","volume":"27","author":"Fattal","year":"2008","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2991290","article-title":"A novel fast single image dehazing algorithm based on artificial multiexposure image fusion","volume":"70","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, J., Tang, Y., Chen, Y., Deng, Z., and Zeng, Z. (2026). An image-based visibility detection method for haze and foggy weather based on dark channel prior. Proceedings of the International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2025), SPIE.","DOI":"10.1117\/12.3101561"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"207275","DOI":"10.1109\/ACCESS.2020.3038239","article-title":"A novel image dehazing algorithm via adaptive gamma-correction and modified AMEF","volume":"8","author":"Zhuang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, R.Q., Duan, Z.P., Guo, C.L., Chai, Z., and Li, C. (2023, January 11\u201315). Ridcp: Revitalizing real image dehazing via high-quality codebook priors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR52729.2023.02134"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jain, N., Jong, A., Scherer, S., and Gkioulekas, I. (2025). SmokeSeer: 3D Gaussian Splatting for Smoke Removal and Scene Reconstruction. arXiv.","DOI":"10.1109\/3DV69130.2026.00055"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104802","DOI":"10.1016\/j.dsp.2024.104802","article-title":"Cdan: Convolutional dense attention-guided network for low-light image enhancement","volume":"156","author":"Shakibania","year":"2025","journal-title":"Digit. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111276","DOI":"10.1016\/j.compeleceng.2026.111276","article-title":"E-IceNet: Adaptive parameter-based dynamic enhancement method for low-light images","volume":"138","author":"Wang","year":"2026","journal-title":"Comput. Electr. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2629827","DOI":"10.1080\/19479832.2026.2629827","article-title":"An innovative deep vectorised scaling Swin connectivity U-Net attention network for disaster detection in satellite imagery","volume":"17","author":"Tolye","year":"2026","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10535","DOI":"10.1109\/TPAMI.2023.3261282","article-title":"Visible and infrared image fusion using deep learning","volume":"45","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, S., Han, M., Qin, Y., and Li, Q. (2024). Self-attention progressive network for infrared and visible image fusion. Remote Sens., 16.","DOI":"10.3390\/rs16183370"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106085","DOI":"10.1016\/j.infrared.2025.106085","article-title":"ECAFusion: Infrared and visible image fusion via edge-preserving and cross-modal attention mechanism","volume":"2025","author":"Peng","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., and Luo, Z. (2022, January 18\u201324). Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Li, P., and Zhang, J. (2020). DIDFuse: Deep image decomposition for infrared and visible image fusion. arXiv.","DOI":"10.24963\/ijcai.2020\/135"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, X., Ren, W., Shen, L., Wan, S., Zhang, J., and Jiang, Y.M. (2025). Bringing RGB and IR together: Hierarchical multi-modal enhancement for robust transmission line detection. arXiv.","DOI":"10.1109\/TASE.2026.3706642"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Yao, R., Yang, Q., Ding, Y., Zhang, L., and Liu, J. (2026). A Lightweight Fire Detection Framework for Edge Visual Sensors Using Small-Sample Domain Adaptation. Sensors, 26.","DOI":"10.3390\/s26041121"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"42816","DOI":"10.1109\/ACCESS.2024.3378568","article-title":"Yolov1 to v8: Unveiling each variant\u2013a comprehensive review of yolo","volume":"12","author":"Hussain","year":"2024","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14032","DOI":"10.1038\/s41598-025-96314-x","article-title":"Research on object detection and recognition in remote sensing images based on YOLOv11","volume":"15","author":"He","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_31","first-page":"102912","article-title":"Object detection from UAV thermal infrared images and videos using YOLO models","volume":"112","author":"Jiang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127685","DOI":"10.1016\/j.neucom.2024.127685","article-title":"Multi-YOLOv8: An infrared moving small object detection model based on YOLOv8 for air vehicle","volume":"588","author":"Sun","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_33","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:23:00Z","timestamp":1788783780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175670"],"URL":"https:\/\/doi.org\/10.3390\/s26175670","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]}}}