Small vehicle classification in the wild using generative adversarial network

With the popularization of intelligent transportation system, the demand for vision-based algorithms and performance becomes more and severe. Vehicle detection techniques have made great strides in the past decades; however, there are still some challenges, such as the classification of tiny vehicles. The images of distant vehicles are generally blurred and lack detailed information due to their low resolutions. To solve this problem, we propose a novel method to generate high-resolution (HR) images from fuzzy images by employing a generative adversarial network (GAN). In addition, the dataset used for training standard GAN is generally constructed by down-sampling from the neutral HR images. Unfortunately, the effect of reconstruction is more modest. To cope with this trouble, we first construct our dataset by using three fuzzy kernels. Then, the exposure of the low-resolution (LR) image is adjusted randomly. Furthermore, a hybrid objective function is designed to guide the model to restore image details. The experimental results on the KITTI data set verify the effectiveness of our method for tiny vehicle classification.

Automated summary

With the popularization of intelligent transportation systems, the demand for vision-based algorithms and performance increases. Vehicle detection techniques have advanced, but face challenges, leading to the proposal of a method using GAN to generate high-resolution images.