摘要
目标检测是当下计算机视觉领域的研究热点,随着深度学习的发展,基于深度学习的目标检测算法的应用越来越多,性能也不断被提升,通过总结目标检测过程中遇到的常见难题以及相应的改进方法,梳理了基于深度学习的目标检测方法的最新研究进展,重点针对基于深度学习目标检测算法的两大类型进行综述。此外还从注意力机制、轻量型网络、多尺度检测等方面对目标检测算法的最新改进思路进行总结梳理。针对当前目标检测领域存在的问题,对其未来的发展趋势进行展望,并提出可行的解决方案,以期为该领域后续的研究工作提供可借鉴的思路和方向。
Object detection is currently a research hotspot in the field of computer vision.With the development of deep learning,object detection algorithms based on deep learning are increasingly applied and their performance is constantly improved.This paper summarizes the latest research progress of object detection methods based on deep learning by summarizing common problems encountered in the process of object detection and corresponding improvement methods.This paper focuses on two types of object detection algorithms based on deep learning.In addition,the latest improvement ideas of target detection algorithms are summarized from the aspects of attention mechanism,lightweight network,multiscale detection.Finally,in view of the current problems in the field of target detection,the future development trend is prospected.And the feasible solution is put forward in order to provide reference ideas and directions for the follow-up research work in this field.
作者
杨锋
丁之桐
邢蒙蒙
丁波
YANG Feng;DING Zhitong;XING Mengmeng;DING Bo(Assets and Equipment Department,Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan 250013,China;School of Traditional Chinese Medicine,Shandong University of Traditional Chinese Medicine,Jinan 255424,China;Department of Medicine and Engineering,China Rehabilitation Research Center,Beijing 100071,China)
出处
《计算机工程与应用》
CSCD
北大核心
2023年第11期1-15,共15页
Computer Engineering and Applications
基金
国家自然科学基金面上项目(81973981)
教育部高等教育司产学合作协同育人项目(202002216003)。
关键词
目标检测
深度学习
计算机视觉
注意力机制
多尺度检测
object detection
deep learning
computer vision
attention mechanism
multi-scale detection
