语义快速增强用于半监督的低光突出的物体检测
IEEE transactions on neural networks and learning systems
|April 11, 2025
概括
这项研究引入了一种新的半监督方法,用于低光突出物体检测 (SOD). 该方法有效地提高了对象在黑暗场景中的可见性,减少了大量手动数据标签的需求.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 现有的突出物体检测 (SOD) 模型在低光条件下扎,原因是训练数据不足和功能集成限制.
- 低光场景对准确的数据注释构成重大挑战,阻碍了强大的SOD模型的开发.
研究的目的:
- 为低光突出物体检测 (SOD) 开发一个有效的半监督框架.
- 通过增强上下文信息和减轻低光环境中的注释负担来解决当前SOD模型的局限性.
主要方法:
- 一个亮度Retinex增强器 (BRE) 的设计旨在减轻照明对SOD任务的影响.
- 一个半监督框架利用稀疏的标记语义提示来增加未标记的数据,结合Retinex分解和上下文引导编码器 (CGE).
- 在标记和未标记数据上,共享和扰乱解码器之间进行了联合一致性训练.
主要成果:
- 拟议的半监督模型显著提高了低光时的SOD性能.
- 这种方法减轻了与低光条件相关的大量数据注释负担.
- 实验结果显示,与最先进的完全监督的SOD模型相比,在多个数据集中具有高度竞争力的性能.
结论:
- 开发的半监督框架为低光突出物体检测提供了一个有希望的解决方案.
- 这种方法有效地平衡了性能提升与减少注释要求,使其适用于现实世界低光场景.
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