ATTENTION-ASTFF-NET: A CHANNEL-AWARE ADAPTIVE SPATIOTEMPORAL FUSION NETWORK FOR VEHICLE RECOGNITION
DOI:
https://doi.org/10.55766/sujst12506Keywords:
Adaptive Feature Fusion Network, Adaptive Spatiotemporal Fusion Network, Channel-Wise Attention, Vehicle Make Recognition, Vehicle Type RecognitionAbstract
Vehicle recognition systems face challenges in simultaneously capturing spatial appearance characteristics and temporal structural patterns. This study proposes Attention-ASTFF-Net, a channel-aware adaptive spatiotemporal fusion network that enhances vehicle type and vehicle make recognition. The proposed framework extracts spatial features with a convolutional backbone and temporal representations with a convolutional 1D-based module. An adaptive fusion mechanism then combines these features to obtain a comprehensive spatiotemporal representation. Channel-wise attention mechanisms, including Efficient Channel Attention (ECA-Net), Gated Channel Transformation (GCT), Squeeze-and-Excitation (SE) Networks, and Selective Kernel Networks (SKNet), are integrated to emphasize important feature channels and improve discriminative capability. Experimental results on the Vehicle Type Image Dataset Version 2 (VTID2) and the Vehicle Make Image Dataset (VMID) show that the proposed network consistently outperforms the baseline ASTFF-Net and other convolutional neural network architectures. Channel-wise attention integration improves localization, feature selectivity, and prediction stability. The proposed network achieves over 99% accuracy on VTID2 compared with approximately 96% achieved by previous methods, and over 97% on VMID compared with 93.11% achieved by existing approaches. Furthermore, Attention-ASTFF-Net with the SE module demonstrates strong computational efficiency at 5.113 giga floating point operations.
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