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在工业接场景中使用Retinex启发的照明曲线估计

Chenbo Shi¹, Xiangyu Zhang¹, Delin Wang¹

¹College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China.

Sensors (Basel, Switzerland)

|August 28, 2025

概括

这项研究引入了基于Retinex的新型网络,用于增强机器人轨迹规划的低光接图像. 这种方法在黑暗环境中提高了可见性和精度,这对于工业应用至关重要.

科学领域:

机器人技术
计算机视觉
图像处理

背景情况:

接爬行机器人需要精确的轨迹规划,
这些环境中的图像质量差会降低激光追踪的特征提取和异常检测.

研究的目的:

专门为机器人接覆盖场景开发低光图像增强网络.
在黑暗环境中提高接特征提取和轨道异常检测的准确性.

主要方法:

一个基于Retinex的网络和一个照明曲线估计模块被提出.
该网络不需要配对或不配对的参考图像进行培训,从而减少数据集的依赖.
它可以自适应地调整亮度,恢复细节,并抑制低光图像中的噪音.

主要成果:

拟议的方法在PSNR,SSIM和LPIPS指标中优于公开 (LOLv1,LOLv2) 和自行收集的接数据集的现有方法.
在低光条件下实现了95.1%的IOU和98.9%的精度.
这种增强有效地支持机器人接的下游任务.

结论:

基于Retinex的网络为机器人接提供了有效的低光图像增强.
这种方法显著提高了轨道规划的特征提取和异常检测性能.
这种解决方案对于在艰难,黑暗的工业环境中增强机器人接操作非常有价值.

关键词:

计算机摄影曲线估计实时低光增强接方式零参考学习

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