A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection

Abstract

In recent years, the deep learning object detection algorithms using 2D images have become the powerful tools for road object detection in autonomous driving. In fact, the deep learning methods for road vehicle detection have achieved the remarkable results. Although there have been a large number of studies that thoroughly explored various types of deep learning methods for vehicle detection, there are a few studies that compare and evaluate the detection time and detection accuracy of the mainstream deep learning object detection algorithms for vehicle detection. Here, this article compares five mainstream deep learning object detection algorithms in vehicle detection, namely the faster RCNN, R-FCN, SSD, RetinaNet, and YOLOv3 on the KITTI data and analyze the obtained results. The detection time and detection accuracy of the five object-detection algorithms on the KITTI test set are compared and analyzed; the PR curve and AP value are used to evaluate the detection accuracy.