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However, image degradation in such environments severely disrupts visual feature extraction, leading to frequent false positives and missed detections. To address this challenge, we propose a novel Multi\u2010scale Adaptive Network (MANet) for robust object detection in low\u2010light scenarios. MANet comprises two main components: a cascaded feature extraction network built upon our proposed multi\u2010scale feature extractor, and an adaptive fusion network that integrates our adaptive feature extractor and a fast normalized fusion module. Additionally, we introduce a joint loss function to further improve classification performance in complex lighting conditions. Experimental results show that MANet achieves an mA\n                    <jats:italic>p<\/jats:italic>\n                    @0.5 of 0.718 and an mA\n                    <jats:italic>p<\/jats:italic>\n                    @0.5:0.95 of 0.451 on the ExDark data set, and also delivers competitive performance on DARKFACE, DUO, and TrashCan. In addition, MANet demonstrates strong cross\u2010scene generalization under real\u2010world low\u2010light conditions. These results validate the effectiveness of MANet in reducing false positives and missed detections, enhancing detection robustness, and laying a solid foundation for robotic perception and decision\u2010making in real\u2010world outdoor low\u2010light environments.\n                  <\/jats:p>","DOI":"10.1002\/rob.70064","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T11:48:46Z","timestamp":1756813726000},"page":"796-815","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi\u2010Scale Adaptive Network for Low\u2010Light Object Detection"],"prefix":"10.1002","volume":"43","author":[{"given":"Jiayu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Xi'an Polytechnic University, and the Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an Polytechnic University Xi'an Shaanxi China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0058-8284","authenticated-orcid":false,"given":"Xiaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Xi'an Polytechnic University, and the Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an Polytechnic University Xi'an Shaanxi China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9869-014X","authenticated-orcid":false,"given":"Yingjian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science Xi'an Polytechnic University Xi'an Shaanxi China"},{"name":"Shaanxi Key Laboratory of Clothing Intelligence Xi'an Shaanxi China"}]},{"given":"Guanqun","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Xi'an Polytechnic University, and the Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an Polytechnic University Xi'an Shaanxi China"}]},{"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Xi'an Polytechnic University, and the Xi'an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an Polytechnic University Xi'an Shaanxi China"}]}],"member":"311","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21667"},{"key":"e_1_2_11_3_1","first-page":"12470","volume-title":"Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision. 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