{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:22:04Z","timestamp":1774585324320,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Pedestrian detection in intelligent transportation systems has made significant progress but faces two critical challenges: (1) insufficient fusion of complementary information between visible and infrared spectra, particularly in complex scenarios, and (2) sensitivity to illumination changes, such as low-light or overexposed conditions, leading to degraded performance. To address these issues, we propose PedDet, an adaptive spectral optimization complementarity framework which specifically enhanced and optimized for multispectral pedestrian detection. PedDet introduces the Multi-scale Spectral Feature Perception Module (MSFPM) to adaptively fuse visible and infrared features, enhancing robustness and flexibility in feature extraction. Additionally, the Illumination Robustness Feature Decoupling Module (IRFDM) improves detection stability under varying lighting by decoupling pedestrian and background features. We further design a contrastive alignment to enhance intermodal feature discrimination. Experiments on LLVIP and MSDS datasets demonstrate that PedDet achieves state-of-the-art performance, improving the mAP by 6.6 % with superior detection accuracy even in low-light conditions, marking a significant step forward for road safety.<\/jats:p>","DOI":"10.3233\/faia250915","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:49Z","timestamp":1761126349000},"source":"Crossref","is-referenced-by-count":4,"title":["PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection"],"prefix":"10.3233","author":[{"given":"Rui","family":"Zhao","sequence":"first","affiliation":[{"name":"JD.com"}]},{"given":"Zeyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Australian National University"},{"name":"La Trobe University"}]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[{"name":"Central South University"}]},{"given":"Yi","family":"Yao","sequence":"additional","affiliation":[{"name":"NavInfo Co., Ltd."}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"}]},{"given":"Wenxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Science"}]},{"given":"Zirui","family":"Song","sequence":"additional","affiliation":[{"name":"University of Technology Sydney"},{"name":"Mohamed bin Zayed University of Artificial Intelligence"}]},{"given":"Xiuying","family":"Chen","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"La Trobe University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:49Z","timestamp":1761126349000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250915","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}