FUSED-Net: Enhancing Few-Shot Traffic Sign Detection with Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation

• URL: http://arxiv.org/abs/2409.14852v1
• Date: Mon, 23 Sep 2024 09:34:42 GMT
• Title: FUSED-Net: Enhancing Few-Shot Traffic Sign Detection with Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation
• Authors: Md. Atiqur Rahman, Nahian Ibn Asad, Md. Mushfiqul Haque Omi, Md. Bakhtiar Hasan, Sabbir Ahmed, Md. Hasanul Kabir,
• Abstract summary: We present 'FUSED-Net', built-upon Faster RCNN for traffic sign detection. Unlike traditional approaches, we keep all parameters unfrozen during training, enabling FUSED-Net to learn from limited samples. We achieve 2.4x, 2.2x, 1.5x, and 1.3x improvements of mAP in 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively.
• Score: 2.111102681327218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Traffic Sign Recognition is paramount in modern transportation systems, motivating several research endeavors to focus on performance improvement by utilizing large-scale datasets. As the appearance of traffic signs varies across countries, curating large-scale datasets is often impractical; and requires efficient models that can produce satisfactory performance using limited data. In this connection, we present 'FUSED-Net', built-upon Faster RCNN for traffic sign detection, enhanced by Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation while reducing data requirement. Unlike traditional approaches, we keep all parameters unfrozen during training, enabling FUSED-Net to learn from limited samples. The generation of a Pseudo-Support Set through data augmentation further enhances performance by compensating for the scarcity of target domain data. Additionally, Embedding Normalization is incorporated to reduce intra-class variance, standardizing feature representation. Domain Adaptation, achieved by pre-training on a diverse traffic sign dataset distinct from the target domain, improves model generalization. Evaluating FUSED-Net on the BDTSD dataset, we achieved 2.4x, 2.2x, 1.5x, and 1.3x improvements of mAP in 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively compared to the state-of-the-art Few-Shot Object Detection (FSOD) models. Additionally, we outperform state-of-the-art works on the cross-domain FSOD benchmark under several scenarios.

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