A Lightweight Method for Vehicle Classification Based on Improved Binarized Convolutional Neural Network

Abstract

Vehicle classification is an important part of intelligent transportation. Owing to the development of deep learning, better vehicle classification can be achieved compared to traditional methods. Contemporary deep network models have huge computational scales and require a large number of parameters. Binarized convolutional neural networks (CNNs) can effectively reduce model computational size and the number of parameters. Most contemporary lightweight networks are binarized directly on a full-precision model, leading to shortcomings such as gradient mismatch or serious accuracy degradation. To addresses the inherent defects of binarization networks, herein, we adjust and improve residual blocks and propose a new pooling method, which is called absolute value maximum pooling (Abs-MaxPooling). The information entropy after weight binary quantization is used to propose a weight distribution binary quantization method. A binarized CNN-based vehicle classification model is constructed, and the weights and activation values of the model are quantified to 1 bit, which saves data storage space and improves classification accuracy. The proposed binarized model performs well on the BIT-Vehicle dataset and outperforms some full-precision models.