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To address this issue, we propose a dual\u2010branch enhancement and multi\u2010modal fusion network for object detection in low\u2010light visible polarization images in dense smog environments. Specifically, the network consists of an image enhancement stage and an object detection stage. In the image enhancement stage, a dual\u2010branch enhancement structure comprising greyscale feature map prediction and atmospheric light transmission network is proposed to remove noise from the images and enhance texture information, jointly generating enhanced visible polarization images. In the object detection stage, feature maps of the enhanced visible polarization images and the degree of visible polarization images are fused, and their fused texture\u2010enhanced feature maps are fed into the detection module for object detection. Additionally, we have collected a dataset of low\u2010light visible polarization images under real smog conditions. Extensive experiments demonstrate that our method can generate visually improved enhanced images and significantly increase detection accuracy and the number of detected objects in low\u2010light and dense smog\u00a0environments.<\/jats:p>","DOI":"10.1049\/ipr2.70234","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T10:17:47Z","timestamp":1760696267000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual\u2010Branch Enhancement and Multi\u2010Modal Fusion for Low\u2010Light Visible Polarization Image Object Detection in Dense Smog Environments"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-7373","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Intelligent Science and Technology Hefei Preschool Education College Hefei Anhui 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