LIGHTWEIGHT RESIDUAL LAYERS BASED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC SIGN RECOGNITION
Abstract
System for Traffic Sign Recognition and Classification is significantly important for especially traffic safety, traffic surveillance, artificial driver services and by all means, for self-driving cars. Traffic sign recognition plays an important role to tackle the traffic related obstacles. And, as traffic sign recognition is particularly applied to portable devices, lightweight models are essential aspect of the agenda. To overcome the mentioned problems, we propose lightweight convolutional neural networks with residual blocks based deep learning model for traffic recognition systems. We not only present the model efficiency but also show the several conducted experiments will well known deep CNN architectures over publicly available German traffic sign recognition benchmark. Our model showed 99.9 % accuracy by F-score, exceeding other models. At last, our model shows generally validity for traffic sign classification problem.