Authors
Md Atiqur Rahman, Nahian Ibn Asad, Md Mushfiqul Haque, Md Bakhtiar Hasan, Sabbir Ahmed, Md Hasanul Kabir
Published in
PloS one. Volume 21. Issue 10. Pages e0359293. Epub Oct 07, 2026.
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 Faster RCNN with Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain 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.
PMID:
42842623
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.
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