Indian Traffic Sign Detection and Classification Through a Unified Framework

Traffic sign boards are vital in facilitating smart transportation systems. More than 90% of accidents happen due to drivers’ inattentiveness over these boards. Hence, relaying traffic sign board information automatically to drivers becomes crucial to avoid such accidents. While numerous traffic sign detection and classification systems exist, it is important to note that these automated systems have not been adequately assessed within the context of Indian settings. The task of traffic sign detection and classification presents unique challenges in the Indian context due to the presence of multiple variations of signboards for a single action (e.g., left turn and right turn). This paper proposes a novel deep neural network architecture for detecting and classifying traffic signboards simultaneously. In the proposed architecture, several state-of-the-art convolutional neural networks such as AlexNet, VGG-19, ResNet-50, and EfficientNet v2 are used as a backbone to solve the task of bounding box regression and sign classification. Along with the proposed method, we collected the Indian traffic signs and information boards dataset. The collected dataset consists of 4257 raw images without any augmentation. We comprehensively evaluated the proposed algorithm on the collected dataset, resulting in the detection and classification accuracies of
$85.5$
% and
$98.5$
%, respectively. The proposed algorithm has also been evaluated on the existing traffic sign recognition dataset, and a comparison with state-of-the-art algorithms is done for both traffic sign detection and classification, demonstrating the effectiveness of the proposed multi-task network.