A Review of Deep Learning Based Object Detection Algorithms

Abstract

With the rapid development of artificial intelligence technology, the traditional manual feature-based object detection method has been gradually replaced by deep learning-based object detection technology. Object detection is the basis and prerequisite for many computer vision tasks aimed at recognizing targets in an image and determining their class and location. The purpose of object detection algorithm research based on deep learning is to improve the detection accuracy and detection speed of the detection algorithm, so that the object detection algorithm can be more safe and convenient applied to People's Daily production and life. This article reviews the evolution of object detection algorithms, focusing on two-stage, one-stage and object detection algorithms based on the transformer architecture. In addition, the performance, advantages and disadvantages of different algorithms are compared, and the development trend of object detection algorithms based on deep learning is summarized in the end.