앙상블 기반 자동차 탐지 정확도-속도 트레이드오프 개선

초록

While detecting surrounding vehicles in autonomous driving is possible with advances of object detection using deep learning, there are cases where small-sized vehicles not being detected. Also, real-time processing requirement should be satisfied to be implemented into autonomous vehicles. However, detection accuracy and execution speed have inverse proportion relationship. To improve the accuracy-speed tradeoff, this study proposes an ensemble method. An input image is down sampled first, and vehicle detection result is acquired for the down sampled image through an object detector. Then, perspective transformation or upsampling is performed on RoI(Region of Interest) where the small-sized vehicles are located, and small-sized vehicle detection result is acquired for the transformed image through another object detector. To validate the proposed method efficiency, the experiment was conducted with Argoverse vehicle data used in autonomous vehicle contest and the accuracy-speed tradeoff was shown to improve by up to 44% through the proposed ensemble method.

키워드