Review of Research on Imbalance Problem in Deep Learning Applied to Object Detection

Abstract

The current scheme of manually extracting features for object detection has been replaced by deep learning. Deep learning technology has greatly promoted the development of object detection technology. Object detection has also become one of the most important application fields of deep learning. Object detection is to simultaneously predict the category and position of object instances in a given image. This technology has been widely used in medical imaging, remote sensing technology, monitoring and security, automatic driving and other fields. However, with the diversification of object detection application fields, the imbalance problem in the application of deep learning to object detection has become a new entry point to optimize the object detection training model. This paper mainly analyzes the use of machine learning technology to solve the object detection problem. There are four kinds of imbalance problems in each training stage of the model: data imbalance, scale imbalance, relative space imbalance and classification and regression imbalance. This paper analyzes the main reasons for the problem, studies representative classical solutions, and expounds the problems existing in object detection in various fields. By analyzing and summarizing the object detection imbalance problems, this paper discusses the directions of the imbalance of object detection in the future.

Keywords

• |deep learning|object detection|data imbalance|scale imbalance|relative spatial imbalance|classifi-cation and regression imbalance