Bangladeshi Road Traffic Sign Detection and Navigation Using Deep Learning

Bangladeshi road traffic sign detection is vital for enhancing road safety and aiding autonomous driving systems by accurately identifying and interpreting local traffic signs. This research focuses on the development of deep learning techniques for Bangladeshi road traffic sign identification and Support, so as to improve the safety of roads and better organize traffic flow. The dataset 2710 raw images were selected and then augmented to 5420 images with specific traffic signs existing in Bangladesh containing 18 categories. The study aims to compare the results of four Transfer Learning model including Xception, VGG19, Inception-ResNetV2, and MobileNetV2 with a novel CNN architecture. Qualitative results reveal that Proposed CNN has achieved the maximum accuracy of 99.54%, surpassing other architectures. Most of the features developed to pre-process data such as normalization and data augmentation to enhance the quality of the dataset and improve the models performance.