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Many studies in this field have demonstrated that image quality significantly contributes to the precision of detection. However, unexpected factors such as adverse weather, occlusions, and scale variations, which extremely weaken the main features of the detected objects, leading to a decrease in detection accuracy. To address these problems, we propose a Feature-enriched Image Detection-YOLO (FID-YOLO), to improve pedestrian detection performance in complex environments by integrating visible and infrared light information. Specifically, we design an illumination-aware image fusion module for visible and infrared image information fusion to generate a new image within more information to enrich pedestrian features. Then, a cascaded feature aggregation module using reparameterization and channel shuffle is introduced to enhance the model\u2019s understanding and generalization capabilities for complex scenes. Furthermore, we exploit a scale-adaptive feature detection head for YOLO detector, which solves the problem of detecting small objects at varying object scales. Experiments on M3FD and LLVIP datasets demonstrate that FID-YOLO outperforms the benchmark models in pedestrian detection. Additionally, we validate the indispensability of each proposed module through ablation experiments.<\/jats:p>","DOI":"10.1371\/journal.pone.0342054","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:43:39Z","timestamp":1772736219000},"page":"e0342054","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["FID-YOLO: A pedestrian detection model integrating multispectral information in complex environments"],"prefix":"10.1371","volume":"21","author":[{"given":"Di","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xilong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5165-6681","authenticated-orcid":true,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"pone.0342054.ref001","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":"DK Jain","year":"2023","journal-title":"Information Fusion."},{"issue":"4","key":"pone.0342054.ref002","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian detection: an evaluation of the state of the art","volume":"34","author":"P Doll\u00e1r","year":"2012","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"issue":"4","key":"pone.0342054.ref003","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.sigpro.2010.08.010","article-title":"Efficient HOG human detection","volume":"91","author":"Y Pang","year":"2011","journal-title":"Signal Processing."},{"issue":"4","key":"pone.0342054.ref004","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.compeleceng.2013.12.004","article-title":"Pedestrian detection and tracking using temporal differencing and HOG features","volume":"40","author":"T Barbu","year":"2014","journal-title":"Computers & Electrical Engineering."},{"key":"pone.0342054.ref005","doi-asserted-by":"crossref","unstructured":"Wang X, Han TX, Yan S. 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