对象检测的演变从CNN到变压器和多式联络融合
Zeran Wang1, Yuan Chen2, Yuhao Gu3
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific reports
|February 5, 2026
概括
本调查比较了卷积神经网络 (CNN) 和基于变压器的对象检测方法. 它强调了它们的优势,分析了多模式融合,并讨论了规模变化和领域适应等挑战.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对象检测对于定位和分类图像内容至关重要.
- 现代方法主要使用卷积神经网络 (CNN) 和变压器架构.
- 了解它们的比较优势是推动该领域发展的关键.
研究的目的:
- 提供现代物体检测方法的全面调查.
- 为了比较基于CNN和基于变压器的范式,详细说明它们的权衡.
- 分析多模融合技术,并确定未来的研究方向.
主要方法:
- 对于物体检测而言,CNN和变形机的结构性比较.
- 分析整合RGB,LiDAR和语言嵌入的多模式融合.
- 在各种数据集中综合最近的基准 (2024-2025年).
主要成果:
- 在地方特征提取和效率方面,CNN卓越;在全球范围内,通过自我注意力,变压器卓越.
- 实时检测变压器 (RT-DETR) 实现了53.1%的mAP;YOLOv8实现了50.2%的mAP.
- 实时探测器超过100 FPS,红外方法在NUAA-SIRST上达到92.45%的F测量.
结论:
- 在尺度变化,封闭和域适应方面仍然存在重大挑战.
- 多模式融合为改进物体检测提供了有前途的途径.
- 本调查为对象检测的研究人员和从业人员提供了一个结构化的参考.
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