Transfer Learning Models for Traffic Sign Recognition System

Abstract

Computer vision has become the most happening technology in Artificial Intelligence in recent decades, and the continued advancement of Intelligent Transportation has been a major consequence of this success. Traffic signs detection, recognition and classification is an important functionality of the Advanced Driver Assistance System (ADAS) which helps the Smart transportation system to identify the traffic, signals and signs on road and perform necessary actions. Extensive research shows that artificial neural networks are an ideal choice for image classification and recognition challenges. Transfer learning has taken Deep learning to another level by enabling the models trained on a task to be re-used for another task which in result reduces the development and training time of the model. Five efficient transfer learning models are explored which are available in Keras libraries - Xceptionnetwork, InceptionV3 Networks, Residual Networks ResNet50, VGG-16 and EfficientNetB0 models for the detection, recognition, and classification of GTSRB traffic signs. The main focus of this paper is to apply and as well as compare five recent and most successful deep learning strategies to verify which model can stand-out in feature extraction and classification of the traffic sign data available. Accuracy, loss, training time and model parameters are considered in grading these models. Xception network has been proven to be highly successful in terms of accuracy (95.04%), minimum lossvalue (0.2311) and affordable speed and training time, whereas ResNet50 and EfficientNetB0 obtained good accuracy with fewer model parameters for traffic signs detection, recognition and classification.