Review of One-Stage Universal Object Detection Algorithms in Deep Learning

In recent years, object detection algorithms have gradually become a hot research direction as a core task in the field of computer vision. They enable computers to recognize and locate target objects in images or video frames, and are widely used in fields such as autonomous driving, biological individual detection, agricultural detection, medical image analysis, etc. With the development of deep learning, general object detection algorithms have shifted from traditional object detection methods to object detection methods based on deep learning. The general object detection algorithms under deep learning are mainly divided into one-stage object detection and two-stage object detection. This paper takes one-stage object detection as the starting point and analyzes and summarizes the mainstream one-stage detection algorithms of the first one-stage object detection algorithm YOLO series (YOLOv1 to YOLOv11, YOLO main improved version), SSD, and DETR series based on Transformer architecture, based on the use of two different architectures: classical convolution and Transformer. This paper introduces the network structure and research progress of various algorithms, summarizes their characteristics, advantages, and limitations based on their structures, summarizes the main common datasets and evaluation indicators in the field of object detection, analyzes the performance of various algorithms and their improvement methods, discusses the application status of various algorithms in different fields, and finally looks forward to the future research directions of one-stage object detection algorithms.