DADF: dual-attention distillation framework using adaptive feature fusion for traffic object recognition

Abstract Training lightweight traffic object detectors with knowledge distillation (KD) is crucial for intelligent transportation systems under resource-limited conditions. However, most KD approaches still rely on fixed thresholding to generate binary masks for student feature reconstruction, disrupting semantic continuity in complex traffic scenes. In this work, a dual attention distillation framework (DADF) using Adaptive feature fusion is proposed for traffic object recognition. Instead of binary masks, DADF produces Softmax-based normalized distributions of soft masks along both spatial and channel dimensions, thereby more effectively regulating the continuity of semantic information. To adaptively balance spatial and channel cues, teacher feature variances are utilized for weighting and fusing the masks into a unified attention map. Meanwhile, a multilayer perceptron generator is subsequently used to reconstruct the masked student features. Finally, the distillation process is optimized by minimizing the mean squared error between the reconstructed and teacher features. We extensively validated the effectiveness of the DADF method across multiple datasets and detectors. On Cityscapes, it boosts YOLOv8 mean average precision (mAP) from 41.9% to 44.1%, while cutting parameters and GFLOPs by 73.0% and 71.6%, and raising inference speed from 188.7 to 202.8 FPS. On KITTI, DADF boosts the RT-DETR mAP from 85.8% to 90.5%, even surpassing its teacher model. It also cuts parameters by 31.0%, reduces GFLOPs by 32.5%, and increases speed from 33.8 to 35.3 FPS. These results highlight DADF’s suitability for traffic measurement applications under resource constraints.