Co-occurrence Relationship Encoding via Channel Merging for Vehicle Part Recognition

Vehicle part recognition aims to determine the subcategories of each vehicle part. Existing algorithms consider to recognize each category as independent classification tasks, which ignore the potential co-occurrence relationship between vehicle parts. In addition, it remains challenges to obtain satisfactory results due to the small intra-class difference. In this paper, we propose a part-pair recognition method based on deep learning by utilizing the co-occurrence relationship. Specifically, we construct a deep neural network for vehicle part recognition, which can use the co-occurrence relationship and recognize two vehicle part simultaneously. We also propose a massive dataset of vehicle parts with fully annotated labels for training and testing. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art vehicle recognition algorithms.