As one of the key characteristics of vehicle identity, vehicle logo plays an important role in vehicle monitoring and identification. Due to the complexity of the natural scene, it is still a great challenge to identifying the vehicle logo accurately. At present, there are few open databases and there are many limitations, which lead to the lack of credibility and practicability. In this paper, a new dataset for natural scenes which contains 10 324 images with 67 types of vehicle logos in various acquisition environments was established. Based on this dataset, a vehicle logo recognition method based on target detection and deep learning was proposed. The method includes two major steps:regional positioning of vehicle logo and prediction of vehicle logo type. Experiments show that the proposed method has strong adaptability to complex background, and the overall recognition rate reaches 89.0% in the classification task involving 30 kinds of vehicle logos.