Vehicle Logo Recognition Using Spatial Structure Correlation and YOLO-T

Abstract

The vehicle logo contains the vehicle’s identity information, so vehicle logo detection (VLD) technology has extremely important significance. Although the VLD field has been studied for many years, the detection task is still difficult due to the small size of the vehicle logo and the background interference problem. To solve these problems, this paper proposes a method of VLD based on the YOLO-T model and the correlation of the vehicle space structure. Aiming at the small size of the vehicle logo, we propose a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks. In addition, we design an effective pre-training strategy to improve the detection accuracy of YOLO-T. Aiming at the background interference, we use the position correlation between the vehicle lights and the vehicle logo to extract the region of interest of the vehicle logo. This measure not only reduces the search area but also weakens the background interference. We have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results show that our proposed method achieves high detection accuracy and outperforms the existing vehicle logo detection methods.

Logo recognition of vehicles based on deep convolutional generative adversarial networks

“…Then, they used 6 existing classifiers and 6 detectors based on deep learning to evaluate their dataset and established new baseline performance. Song et al [27] proposed a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks.…”