红外薄弱小目标检测网络:一篇评论
Yongbo Cheng1,2,3, Xuefeng Lai1,2, Yucheng Xia1,2
1Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chinese Academy of Sciences, Chengdu 610209, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本综述涵盖了用于红外暗小目标检测的深度学习,总结了最近的数据集,网络和指标. 它解决了代表性,准确性和复杂背景等关键挑战,以提高检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 红外成像技术 红外成像技术
背景情况:
- 深度学习已经彻底改变了目标检测,为红外暗小目标检测提供了新的方法.
- 现有的方法在表示,准确性,信息丢失和复杂背景方面面临挑战.
研究的目的:
- 为红外暗小目标检测提供深度学习方法的全面审查.
- 总结该领域的最新进展,数据集,网络和评估指标.
- 确定关键的挑战和未来的研究方向.
主要方法:
- 专注于过去三年开发的深度学习方法.
- 基于红外暗小目标检测的六个关键问题对方法进行分类.
- 审查了12个公共数据集和共同的评估指标.
主要成果:
- 量化比较最新的深度学习网络的性能.
- 突出了提高小目标表示和界限框回归精度的进展.
- 讨论了减轻信息丢失和平衡检测性能的策略.
结论:
- 深度学习为红外暗小目标检测提供了显著的进步.
- 解决复杂背景和网络效率等挑战对于实际部署至关重要.
- 该综述为进入或在该领域取得进展的研究人员提供了宝贵的资源.
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