FUSED-Net: Detecting traffic signs with limited data

Abstract

Automatic Traffic Sign Recognition is paramount in modern transportation systems. However, curating large-scale datasets for diverse traffic sign detection remains impractical. In this context, we present FUSED-Net, a novel approach that enhances Few-Shot Object Detection (FSOD) for traffic signs using limited data. FUSED-Net integrates F aster RCNN with U nfrozen Parameters, Pseudo- S upport Sets, E mbedding Normalization, and D omain Adaptation to improve detection accuracy. Unlike conventional methods, FUSED-Net keeps all parameters unfrozen during training, enabling it to learn effectively from limited samples. A Pseudo-Support Set is generated through data augmentation, enhancing performance by compensating for the scarcity of target domain data. Embedding Normalization reduces intra-class variance, standardizing feature representations. Domain Adaptation, achieved by pre-training on a diverse traffic sign dataset, improves model generalization. Experimental results on the BDTSD dataset demonstrate that FUSED-Net achieves 2.4 × , 2.2 × , 1.5 × , and 1.3 × improvements in mAP under 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively, compared to state-of-the-art FSOD models. Additionally, FUSED-Net achieves superior performance on the cross-domain FSOD benchmark across multiple settings. The source code and the URLs to download the datasets are available at https://github.com/180041123-Atiq/FUSED-Net .