A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

Abstract

Video-based Intelligent Transportation Systems (V-ITS) can play an important role in developing a wide range of applications in transportation field. These systems use the outputs of video cameras to extract desired information by the means of various Artificial Intelligence techniques. Considering impressive advantages of applying Deep Neural Networks (DNNs) in different fields of object detection and classification, these methods have attracted a huge attention among researchers in recent years. In this regard, Convolutional Neural Networks (CNNs) as an important class of DNNs have been used for visual imagery goals in a wide variety of applications such as image recognition and video analysis, and even made their way through ITS applications. One of the most important steps of V-ITS applications is the process of vehicle detection in video frames and the high accuracy rate in this step can provide applicable data for other complementary modules such as vehicle tracking and classification. In this paper, a robust method to detect vehicles in video frames based on CNNs is proposed which provides an almost real-time performance and impressive accuracy. To overcome the challenges of building a precise vehicle detection model from still images, we have transformed the main architecture of a pre-trained ResNet-50 residual network to Faster Region-based Convolutional Neural Network (Faster R-CNN). Experimental results show that the system's sensitivity factor is 0.985 and it needs an average of 74 milliseconds to detect vehicles in real condition data. Consequently, our method can provide acceptable results in vehicle detection in terms of accuracy and execution time.