计算机科学与探索2025，Vol.19Issue(8)：2057-2084,28.DOI:10.3778/j.issn.1673-9418.2503011
基于深度学习的目标检测算法轻量化研究综述
Review of Lightweight Object Detection Algorithms Based on Deep Learning
摘要
Abstract
With the rapid development of deep learning,object detection algorithms based on deep learning have been widely used in many fields.However,with the continuous evolution of the algorithm,a series of challenges has gradually emerged:the increase in the complexity of the model leads to the rapid expansion of the number of parameters and the amount of computation,which in turn reduces the running speed and makes it difficult to meet the needs of high-real-time application scenarios.The high requirements of the algorithm on hardware performance limit its efficient deployment in resource-constrained environments such as mobile devices and edge computing,and narrow its application range.The significant rise in training costs,including the need for large computational resources and long training time,also prevents the rapid iteration of models.In order to deal with these challenges,the research of lightweight object detection algorithm comes into being.This paper aims to review the recent progress of lightweight object detection algorithms based on deep learning.In this paper,the task of object detection and its evaluation index are summarized,and then the development history and representative model of object detection algorithm are reviewed in detail.On this basis,the paper focuses on the lightweight technology of object detection algorithm,including lightweight network architecture design to reduce model computational complexity and spatial complexity,lightweight convolutional technology innovation to reduce the number of parameters and computation while maintaining model performance,deep learning model compression method to optimize model structure to reduce storage requirements.Efficient deployment and real-time inference of resource-constrained devices are realized.Finally,this paper summarizes the current research status of lightweight object detection algorithm,and prospects and thinks in the aspects of multi-field technology integration,hardware architecture optimization and edge device deployment.关键词
深度学习/目标检测算法/模型轻量化Key words
deep learning/object detection algorithm/model lightweight分类
信息技术与安全科学引用本文复制引用
董甲东,桑飞虎,郭庆虎,陈琳,郑春香..基于深度学习的目标检测算法轻量化研究综述[J].计算机科学与探索,2025,19(8):2057-2084,28.基金项目
国家自然科学基金(62205005) （62205005）
安徽省高校科研计划重大项目(2024AH040174).This work was supported by the National Natural Science Foundation of China(62205005),and the Anhui University Scientific Research Plan Major Project(2024AH040174). （2024AH040174）
