Study on vehicle color detection and recognition based on deep learning

Color is one of the most features of vehicles and can be used for vehicles recognition. Deep learning has greater advantages for vehicle color recognition over traditional algorithms. In this paper, we present a vehicle color recognition method using GoogLeNet with Inception v1. Inception v1 increases the width and depth of the network, reduces the parameters to save computing resources and uses sparse matrix to avoid redundancy of traditional neural network as well. We use a publicly dataset to train and validate GoogLeNet and a self-made dataset to test the method. The method can recognize regular eight kinds of vehicle colors and the probability is stable at 90%-95%. Afterward, we have a discussion on how the impact of different datasets on the method as well as the possible reasons. In the future, we will combine GoogLeNet and Yolo network structure to research vehicle color recognition.