PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection
Zhao, Rui Zhang, Zeyu Xu, Yi Yao, Yi Huang, Yan Zhang, Wenxin Song, Zirui Chen, Xiuying Zhao, Yang
Zhao, Rui
Zhang, Zeyu
Xu, Yi
Yao, Yi
Huang, Yan
Zhang, Wenxin
Song, Zirui
Chen, Xiuying
Zhao, Yang
Author
Zhao, Rui , Zhang, Zeyu , Xu, Yi , Yao, Yi , Huang, Yan , Zhang, Wenxin , Song, Zirui , Chen, Xiuying , Zhao, Yang
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Citation
R. Zhao et al., “PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection,” pp. 1059–1066, Oct. 2025, doi: 10.3233/FAIA250915
Source
Frontiers in Artificial Intelligence and Applications
Conference
28th European Conference on Artificial Intelligence
Keywords
Subjects
Source
28th European Conference on Artificial Intelligence
Publisher
IOS Press