A Review on Anchor Assignment and Sampling Heuristics in Deep Learning-based Object Detection

Abstract

Deep learning-based object detection is a fundamental but challenging problem in computer vision field, has attracted a lot of study in recent years. State-of-the-art object detection methods rely on the selection of positive samples and negative samples, i.e., called sample assignment, and the definition of a useful set for training, i.e., called sample sampling heuristics. This paper presents a comprehensive review of the advanced anchor assignment and sampling approaches in deep learning-based object detection. Each problem is classified and analyzed systematically. According to the problem-based taxonomy, we identify the advantages and disadvantages of each problem in-depth and present open issues regarding the current methods. Furthermore, this paper also reviews the new trends in solving object detection that has not been discussed during the last two years.