EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration

Abstract

While detecting surrounding vehicles in autonomous driving is possible with advances in object detection using deep learning, there are cases where small vehicles are not being detected accurately. Additionally, real-time processing requirements must be met for implementation in autonomous vehicles. However, detection accuracy and execution speed have an inversely proportional relationship. To improve the accuracy–speed tradeoff, this study proposes an ensemble method. An input image is downsampled first, and the vehicle detection result is acquired for the downsampled image through an object detector. Then, warping or upsampling is performed on the Region of Interest (RoI) where the small vehicles are located, and the small vehicle detection result is acquired for the transformed image through another object detector. If the input image is downsampled, the effect on the detection accuracy of large vehicles is minimal, but the effect on the detection accuracy of small vehicles is significant. Therefore, the detection accuracy of small vehicles can be improved by increasing the pixel sizes of small vehicles in the transformed image more than the given input image. To validate the proposed method’s efficiency, the experiment was conducted with Argoverse vehicle data used in an autonomous vehicle contest, and the accuracy–speed tradeoff improved by up to a factor of two using the proposed ensemble method.

Keywords

• autonomous vehicles
• vehicle detection
• image processing
• deep learning
• model ensemble