Bangladeshi traffic sign recognition using the lightweight BanTrafficNet deep learning approach

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Abstract
Traffic sign recognition is a critical aspect of intelligent transportation systems that enhances road safety, traffic management, and driver assistance. Notably, while significant research efforts have been devoted to many prevalent traffic signs, Bangladeshi sign recognition remains relatively underexplored due to a lack of traffic sign knowledge and unawareness of the sector individuals. The scarcity of Bangladeshi traffic sign datasets has exacerbated this limitation, hindering progress. Moreover, most Bangladeshi traffic sign research predominantly relies on a limited number of signs, leading to diminished accuracy and the inability to detect all traffic signs. Additionally, existing systems grapple with suboptimal performance accuracy and heightened computational complexity, further emphasizing the research gap. We propose a robust Bangladeshi traffic sign recognition system to address these formidable challenges using a deep learning-based Convolutional Neural Network (CNN) architecture. The main aim is to create a lightweight model that helps identify traffic signs correctly with less computational effort. We used the effective combination of various deep learning layers in the proposed system. A significant contribution of our work lies in creating a novel Bangladesh Traffic Sign Recognition (BDTSR) dataset, addressing the scarcity of data in this domain. This dataset comprises comprehensive information, including 48 types of traffic signs, and these images were taken from different roads in the country. Our dataset aims to fill a critical gap in Bangladeshi traffic sign research and provides a solid foundation for more extensive and inclusive studies in the field. Through this innovative approach, we aim to contribute significantly to the field of BDTSR, filling the gaps in traffic sign recognition and bolstering the accessibility of traffic signs within the Bangladeshi individual traffic community and beyond. Our evaluation of the newly created BDTSR dataset and the existing two benchmark datasets resulted in high recognition accuracies of 99.34%, 96.97% and 98.82% accuracies for the BDTSR, German Traffic Sign Recognition Benchmark (GTSRB) and the Belgian Traffic Sign Dataset (BTSC) respectively. These results highlight the superiority of our model in the Bangladeshi Traffic Sign Recognition domain, outperforming existing models in terms of accuracy and computational efficiency.
Funding Information
  • Multimedia University (MMUI/230023.02)

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