Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Abstract

Extracting vehicle information is of great significance to the construction of the Internet of Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle information. Due to the complex traffic environment, existing methods have difficulty to accurately detect the vehicle logo, especially when the vehicle logo has motion blur. To alleviate these problems, a new approach is proposed to detect vehicle logo under motion blur with the combination of Filter-DeblurGAN and VL-YOLO. Filter-DeblurGAN possesses a judgment mechanism, which can determine whether the image needs to be deblurred based on the degree of blur of each image. It can also deblur images with arbitrary resolution. Filter-DeblurGAN solves the defect that DeblurGAN lacks the judgment mechanism and is too harsh on resolution. It can be directly applied to the deblurring process of VLD. We also propose a method using VL-YOLO and Eliminate Outliers Cluster (EOC) algorithm to further improve the detection accuracy. VL-YOLO achieves accurate detection of the vehicle logo by constructing a deeper multi-scale detection network and using the initial candidate boxes provided by EOC algorithm. The EOC algorithm is not affected by outliers. In order to further promote the research of VLD, we have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results demonstrate that the proposed method achieves good detection accuracy in the environment of motion blur, and outperforms existing methods.