高亮度指导网络 (HLGNet):用于低亮度实例分割的高亮度指导网络,具有空间频域增强的空间频域.
Huaping Zhou1, Tao Wu2, Kelei Sun3
1School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China; School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, Anhui, China; State Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
概括
本研究介绍了HLGNet,这是一个用于低光实例细分的新型网络. 通过结合空间和频域处理,HLGNet增强了图像特征,在具有挑战性的照明条件下显著提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 实例细分模型在正常照明中表现出色,但由于细节有限,在低光下失败.
- 现有的频域方法缺乏空间处理,导致边界划分和局部感知不佳.
研究的目的:
- 为低光条件开发一个有效的实例细分模型.
- 通过整合空间和频率域增强来改善特征表示.
主要方法:
- 拟议的HLGNet (高光引导网络) 使用高光图像罩.
- 引入了空间频率增强 (SPE) 块,用于组合本地空间和全球频率信息.
- 开发了动态亲缘融合 (DAF) 模块和HLG解码器,用于增强特征融合和注意力机制.
主要成果:
- 在低光实例细分方面,HLGNet表现出卓越的性能.
- 该SPE块有效地整合了空间和频域特征.
- DAF模块和HLG解码器改善了详细目标和全球背景的捕获.
结论:
- HLGNet显著提升了低光实例细分能力.
- 拟议的混合空间频率方法解决了先前方法的局限性.
- 该网络在基准数据集上显示了最先进的性能.
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