Ethiopian Traffic Sign Recognition Using Customized Convolutional Neural Networks and Transfer Learning

Abstract

Intelligent transportation systems rely greatly on their capacity to identify and recognize traffic signs. Traffic signs are important for modern transportation systems because they keep roads safe and help drivers, especially in areas like Ethiopia where sign designs are unique and diversified. In this study, we presented a convolutional neural network (CNN)–based model for Ethiopian traffic sign recognition (ETSR) purposes. We applied the transfer learning technique to fine-tune the pretrained models, namely, MobileNet, VGG16, and ResNet50. Both training and model hyperparameters are fine-tuned, and the 11,000 Ethiopian traffic sign images, which have 156 unique signs, are leveraged to build the new models. Optimizer, batch size, learning rate, and epoch are among the tuned training hyperparameters. All convolutional bases (learning layers) are trained using new weights. We built the fully connected layer of each model from two batch normalization layers and two dense layers. The output layer of the models has 156 units (neurons) with a softmax activation layer. The performances of newly developed models are rigorously compared with those of the base (pretrained) models. The best model was also selected after rigorous experiments. Based on the experiment, we achieved testing accuracy of 97.91%, 93.45%, and 80.18% for fine-tuned VGG16, MobileNet, and ResNet50, respectively.