Transfer Learning Using Deep Neural Networks for Classification of Truck Body Types Based on Side-Fire Lidar Data

Vehicle classification is one of the most essential aspects of highway performance monitoring as vehicle classes are needed for various applications including freight planning and pavement design. While most of the existing systems use in-pavement sensors to detect vehicle axles and lengths for classification, researchers have also explored traditional approaches for image-based vehicle classification which tend to be computationally expensive and typically require a large amount of data for model training. As an alternative to these image-based methods, this paper investigates whether it is possible to transfer the learning (or parameters) of a highly accurate pre-trained (deep neural network) model for classifying truck images generated from 3D-point cloud data from a LiDAR sensor. In other words, without changing the parameters of several well-known convolutional neural networks (CNNs), such as AlexNet, VggNet and ResNet, this paper shows how they can be adopted to extract the needed features to classify trucks, in particular trucks with different types of trailers. This paper demonstrates the applicability of these CNNs for solving the vehicle classification problem through an extensive set of experiments conducted on images created based on data from a LIDAR sensor. Results show that using pre-trained CNN models to extract low-level features within images yield significantly accurate results, even with a relatively small size of training data that are needed for the classification step at the end.