计算机科学与探索2025，Vol.19Issue(5)：1115-1140,26.DOI:10.3778/j.issn.1673-9418.2411032
深度学习下的单阶段通用目标检测算法研究综述
Review of One-Stage Universal Object Detection Algorithms in Deep Learning
摘要
Abstract
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 ob-ject 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 eval-uation 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.关键词
目标检测/深度学习/计算机视觉/单阶段/YOLO/DETRKey words
object detection/deep learning/computer vision/one-stage/YOLO/DETR分类
计算机与自动化引用本文复制引用
王宁,智敏..深度学习下的单阶段通用目标检测算法研究综述[J].计算机科学与探索,2025,19(5):1115-1140,26.基金项目
呼和浩特市基础研究与应用基础研究项目(2024-规-基-33) （2024-规-基-33）
内蒙古自然科学基金(2023MS06009) （2023MS06009）
内蒙古高等学校科学研究重点项目(NJZZ21004). This work was supported by the Basic Research and Applied Basic Research Project of Hohhot(2024-Gui-Ji-33),the Natural Science Foundation of Inner Mongolia(2023MS06009),and the Higher Educational Scientific Research Program of Inner Mongolia Autono-mous Region(NJZZ21004). （NJZZ21004）
