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基于深度学习的单目视觉目标检测综述

刘桂超 王怀光 任国全 吴定海

计算机工程与应用2026,Vol.62Issue(1):1-19,19.
计算机工程与应用2026,Vol.62Issue(1):1-19,19.DOI:10.3778/j.issn.1002-8331.2504-0330

基于深度学习的单目视觉目标检测综述

Review of Monocular Vision Object Detection Based on Deep Learning

刘桂超 1王怀光 1任国全 1吴定海1

作者信息

  • 1. 陆军工程大学 石家庄校区,石家庄 050051
  • 折叠

摘要

Abstract

Monocular visual object detection,characterized by its low hardware cost and high real-time performance,has gradually become a core technology in fields such as autonomous driving and intelligent surveillance,playing an indis-pensable role.However,issues such as geometric ambiguity,occlusion robustness,and small object detection accuracy remain significant bottlenecks in current research.This paper primarily focuses on algorithm-level advancements,system-atically quantifying the progress in monocular visual object detection technology from three perspectives:algorithm evolu-tion,performance evaluation,and lightweight design.Firstly,single-stage detection algorithms are deconstructed into classic convolutional architectures and Transformer architectures,analyzing and summarizing their structural innovations and performance bottlenecks,revealing the trade-off patterns between accuracy,speed,and complexity.Secondly,from the three levels of network design,algorithm optimization,and model compression,this paper explores the integration strategies of lightweight technologies with object detection algorithms.It also consolidates multi-dimensional evaluation metrics from the three main official datasets used for training and evaluation in object detection,building a cross-model comparison framework based on the MS-COCO-2017 dataset to conduct a horizontal performance comparison of differ-ent single-stage detectors.Finally,the paper looks ahead to cutting-edge directions such as multimodal fusion and light-weight improvements,aiming to provide systematic references for the engineering application and theoretical break-throughs of monocular visual object detection algorithms.

关键词

单阶段目标检测/深度学习/轻量化模型/经典卷积架构/Transformer架构

Key words

one-stage object detection/deep learning/lightweight model/classic convolution architecture/Transformer architecture

分类

信息技术与安全科学

引用本文复制引用

刘桂超,王怀光,任国全,吴定海..基于深度学习的单目视觉目标检测综述[J].计算机工程与应用,2026,62(1):1-19,19.

基金项目

科技创新研究计划项目([2022]40) ([2022]40)

国家自然科学基金(51305454) (51305454)

陆军工程大学石家庄校区自立项目. ()

计算机工程与应用

1002-8331

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