Recognizing Traffic Signs using Fine-tuning Based Few-Shot Object Detection
Date
2023-05-30
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
The most critical task for the Advanced Driver Assistance System (ADAS) which
is generally used in autonomous vehicles is to develop a reliable and fast Traffic Sign
Recognition (TSR) system. TSR identifies the traffic sign from an image and then
determines its category. The majority of widely used TSR techniques that rely on
deep convolutional neural networks (DCNNs) emphasize on discriminating feature
learning against visual differences of different traffic signs. But these techniques
perform poorly if the number of samples available for each of the category is limited
to model training. To overcome this problem, few-shot learning can be used where
the approach focuses on learning common but distinctive qualities of class-specific
objects with few training samples, as opposed to depending heavily on supervision
to learn discriminating features. In this work, we have used fine-tuning approach
for few-shot learning in order to recognize traffic signs with only a limited number
of samples per category. We have introduced Domain Adaptation, Warm Model,
Pseudo-Support Set and Instance-Level Feature Normalization in our base architec ture. Our model outperformed all state-of-the-art (SOTA) architectures for few-shot
learning across different shot settings, including 2, 3, 5, and 10 shots. Particularly,
our model achieved remarkable results in 3-shot and 5-shot scenarios, with an addi tional mAP improvement of 3.53 and 3.73, respectivel
Description
Supervised by
Dr. Md. Hasanul Kabir,
Professor,
Co-Supervisor
Mr. Sabbir Ahmed,
Assistant Professor,
Department of Computer Science and Engineering(CSE),
Islamic University of Technology(IUT),
Board Bazar, Gazipur-1704, Bangladesh
Keywords
Citation
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