Robust Vehicle and Pedestrian Detection in Adverse Weather via a Weather-Aware Transformer

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Abstract
Pedestrian and vehicle in adverse weather detection technology based on deep learning has attracted increasing attention. However, YOLO-based detectors degrade under low contrast and noise, causing missed small or occluded targets, while DETR-based models suffer from slow convergence and limited multi-scale sensitivity. To address these challenges, a robust weather-aware transformer for vehicle and pedestrian detection, RWT-DETR, is proposed. Firstly, a Strip-Oriented Feature Gating (SOFG) module is designed to improve the capture of strip-like and directional features of traffic targets. Secondly, a Signed Attention Decomposition (SAD) mechanism is designed to enable the encoder to model both positive and negative correlations, thereby enhancing the model's robustness against noise interference and feature blurring. Finally, an Adaptive Multi-scale Fusion with Gating (AMFG) module is proposed to achieve dynamic calibration and denoising of cross-scale features, significantly improving semantic alignment. Experimental results show that, compared with the baseline, the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 2.4% and 3.0%, respectively. Meanwhile, it outperforms existing mainstream models, confirming its effectiveness under adverse weather conditions.
Funding Information
  • Funding Project of National Natural Science Foundation of China (61503005)
  • Funding Project of Humanities and Social Sciences of the Ministry of Education in China (22YJAZH002)

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