提出光照不变特征学习方法，提升低光下的目标检测效果。
You Only Look Around: Learning Illumination Invariant Feature for Low-light Object Detection
- 基于朗伯反射模型，利用邻近像素与通道关系提取光照不变特征。
- 在低光场景下检测性能显著提升，且对正常与过曝场景也有效。
- 模块可无缝集成到现有检测框架，适合低光视觉任务研究者。
本文提出YOLA，一种针对低光环境下目标检测的新框架。不同于以往工作，我们从特征学习角度解决该难题。具体而言，通过朗伯图像生成模型学习光照不变特征。观察发现，在朗伯假设下，可通过邻近颜色通道及空间相邻像素间的相互关系，近似获得光照不变的特征图。引入额外约束后，这些关系可表示为可训练的卷积核，并在网络中以检测驱动方式优化。为此，我们设计了一个专用模块，用于从低光图像中提取光照不变特征，可轻松集成至现有目标检测框架。实验表明，该方法在低光目标检测任务中表现显著提升，同时在正常光照与过曝场景中也取得良好结果。代码已开源：https://github.com/MingboHong/YOLA。
原文摘要 · Abstract (English)
In this paper, we introduce YOLA, a novel framework for object detection in low-light scenarios. Unlike previous works, we propose to tackle this challenging problem from the perspective of feature learning. Specifically, we propose to learn illumination-invariant features through the Lambertian image formation model. We observe that, under the Lambertian assumption, it is feasible to approximate illumination-invariant feature maps by exploiting the interrelationships between neighboring color channels and spatially adjacent pixels. By incorporating additional constraints, these relationships can be characterized in the form of convolutional kernels, which can be trained in a detection-driven manner within a network. Towards this end, we introduce a novel module dedicated to the extraction of illumination-invariant features from low-light images, which can be easily integrated into existing object detection frameworks. Our empirical findings reveal significant improvements in low-light object detection tasks, as well as promising results in both well-lit and over-lit scenarios. Code is available at \url{https://github.com/MingboHong/YOLA}.
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