A Proposed Multi Layer CNN Architecture for Detecting Bangla Traffic Sign

Detecting Bangla traffic signs in Bangladesh is crucial for road safety as it ensures effective communication with drivers who primarily understand Bengali. Accurate recognition of these signs can significantly reduce accidents and enhance compliance with traffic regulations, ultimately saving lives on the country's roads. This study delves into the development of a multi-layer Convolutional Neural Network (CNN) architecture specifically designed for precise Bangla traffic sign detection. The dataset was collected from Dhaka by us and aims to accurately represent the road systems in Bangladesh.We used a range of powerful deep learning algorithms, such as DenseNet201, ResNet50, VGG19, Xception, CNN01, and CNN02, to tackle the traffic sign detection task. These algorithms were selected for their impressive track record in image classification tasksThe models were improved to increase reliability after being trained on a well-rounded dataset collected directly from Dhaka.After a careful examination, DenseNet201 stood out as the best model, achieving an impressive accuracy of 99.65%. This impressive accomplishment highlights the ability of the DenseNet201 architecture to recognize complex features found in Bangla traffic signs.As there is not much research paper about bangla traffic sign detection.So it is hard to compare. There is one paper of Uddin, Md Mahatab et al about bangla traffic sign identification. His CNN model achieved 98% accuracy. Our study addresses a significant gap in the field of Bangla traffic sign detection, especially considering the lack of existing research papers on the subject. In addition, it highlights the significance of choosing CNN architectures that are customized for specific datasets and tasks, thus enhancing the accuracy and effectiveness of machine learning models in real-world scenarios.