{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:06:22Z","timestamp":1780542382281,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CSG Electric Power Research Institute","award":["SEPRI-K22B100"],"award-info":[{"award-number":["SEPRI-K22B100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fusion of infrared and visible images is a well-researched task in computer vision. These fusion methods create fused images replacing the manual observation of single sensor image, often deployed on edge devices for real-time processing. However, there is an issue of information imbalance between infrared and visible images. Existing methods often fail to emphasize temperature and edge texture information, potentially leading to misinterpretations. Moreover, these methods are computationally complex, and challenging for edge device adaptation. This paper proposes a method that calculates the distribution proportion of infrared pixel values, allocating fusion weights to adaptively highlight key information. It introduces a weight allocation mechanism and MobileBlock with a multispectral information complementary module, innovations which strengthened the model\u2019s fusion capabilities, made it more lightweight, and ensured information compensation. Training involves a temperature-color-perception loss function, enabling adaptive weight allocation based on image pair information. Experimental results show superiority over mainstream fusion methods, particularly in the electric power equipment scene and publicly available datasets.<\/jats:p>","DOI":"10.3390\/s24061735","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T08:59:37Z","timestamp":1709801977000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images"],"prefix":"10.3390","volume":"24","author":[{"given":"Bao","family":"Yan","sequence":"first","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2267-480X","authenticated-orcid":false,"given":"Kehua","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Wang","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, China Southern Power Grid, Guangzhou 510063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.inffus.2023.02.014","article-title":"Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes","volume":"95","author":"Jain","year":"2023","journal-title":"Inf. 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