Transfer Learning for classifying front and rear views of vehicles

Abstract

Abstract Various computer systems have been proposed to classify vehicles according to several criteria (category, brand, model). Unfortunately, there is not much research on the classification of views, especially front and rear views. Several factors make this classification very difficult including similarity in shape, size, and color. This work aims to classify front and rear views of vehicles using the Transfer Learning (TL) approach. Here, we used a pre-trained CNN (AlexNet) that has been trained on more than a million images and can classify images into 1000 object categories. Thus, we transferred its learned knowledge and applied it to our new task (Classifying vehicle views). We conducted then two experiments. The first experiment has two scenarios: the first scenario is devoted to Transfer Learning using the AlexNet model, and the second scenario aims to build a network from scratch inspired from AlexNet. Experimental results reveal that the Transfer Learning approach gives high results. On the other hand, in the second experiment, we decided to use TL-AlexNet to extract features and train them with an SVM classifier instead of fully connected layers. And also, we combined the SVM with the fully connected layers. The accuracy rates have been improved after this experiment.