A Large-Scale Benchmark for Vehicle Logo Recognition

The Vehicle Logo Recognition (VLR) is becoming more and more important in the Automated Vehicle Classification (AVC) research area since it can effectively assist vehicle classification. However, existing open-access vehicle logo datasets have many issues, which are hard to represent real-world conditions such as occlusions, low image resolutions and varying lighting conditions, thus heavily hindering the VLR application in reality. For this, a large-scale vehicle logo benchmark is proposed in this paper. Firstly, a large-scale vehicle logo dataset namely VLD 1.0 is collected, which contains 25,189 images of 66 classes. Each type of cars is collected with multiple images from various conditions to comprehensively represent real-world conditions. Secondly, a vehicle logo recognition evolution protocol is designed to fully evaluate performance, i.e., Mean Average Precision (MAP) under different Intersection Over Union (IOU) values and Speed. Thirdly, the YOLOv3 deep learning model is applied as a baseline method and its performance on the VLD 1.0 is reported.