Classification of truck body types using a deep transfer learning approach
Reza Vatani Nezafat, Behrouz Salahshour, Mecit Cetin · 2018
Classification of vehicles is one of the most important tasks in intelligent transportation systems (ITS). While there are various types of sensors for measuring vehicle characteristics, this paper is focused on an image-based vehicle classification system. Most traditional approaches for image-based vehicle classification are computationally extensive and typically require a large amount of data for model training. This paper investigates whether it is possible to transfer the learning of a highly accurate pre-trained model for classifying truck images based on body type. Results show that using a pre-trained model to extract low-level features of images increases the accuracy of the model significantly, even with a relatively small size of training data. Furthermore, a convolutional neural network (CNN) is shown to outperform other types of models to classify trucks based on the extracted features.