I. Introduction
In modern technology age, object detection is a significant technique in computer vision field. Vision-based object detection task identifies and locates objects of a definite class, such as vehicles or pedestrians within an image or video. Detecting various objects in vehicle traffic scenes is become essential part for intelligent transportation system. Therefore, a lot of researchers is continuously innovated to improve the vehicle detection methods. But vehicle detection still facing the challenging problems because of vehicle appearance changes, multi-scale difficulties, occluded conditions, illumination variation and various environment situations. Researchers have addressed these issues through the use of deep learning-based methods. To perform the experiments, researchers are trained and tested on benchmark datasets such as KITTI, UA-DETRAC. The image data in these datasets are run by deep learning algorithms through a lot of layers of neural network algorithms, each of which passes a simplified representation of that data to the next layer.