Bangladeshi Road Traffic Sign Detection And Navigation Using Deep Learning

Abstract

Bangladeshi road traffic sign detection is a must for enhancing road safety and aiding autonomous driving systems by accurately identifying and interpreting local traffic signs. This work focuses on the development of deep learning methods for Bangladeshi road traffic sign identification and Support, so as to improve the safety of roads and better organize traffic flow. The dataset of raw images selected is 2710 which has been carefully augmented to 5420 images with specific traffic signs existing in Bangladesh and distributed over 18 categories. The study aims to discuss and compare the results of four Transfer Learning, namely Xception, VGG19, InceptionResNetV2, and MobileNetV2 with a novel CNN architecture proposed for use. Qualitative results reveal that Proposed CNN has the maximum accuracy of 99.54%, surpassing other architectures. Most of the features developed will be on how to pre-process data such as normalization and data augmentation to enhance the quality of the set and models. Evaluation parameters such as accuracy, precision, recall, and F1-score show that deep learning approaches give a reliable technique for the classification of traffic signs irrespective of the background context. The studies support the idea of deep learning in playing a central role to re-define the intelligent transportation systems through real-time sign recognition and improving the driver safety and traffic management in both complex and simple urban and rural geography of Bangladesh.