Glass surfaces are ubiquitous in daily life, typically appearing colorless, transparent, and lacking distinctive features. These characteristics make glass surface detection a challenging computer vision task. Existing glass surface detection methods always rely on boundary cues (e.g., window and door frames) or reflection cues to locate glass surfaces, but they fail to fully exploit the intrinsic properties of the glass itself for accurate localization. We observed that in most real-world scenes, the illumination intensity in front of the glass surface differs from that behind it, which results in variations in the reflections visible on the glass surface. Specifically, when standing on the brighter side of the glass and applying a flash towards the darker side, existing reflections on the glass surface tend to disappear. Conversely, while standing on the darker side and applying a flash towards the brighter side, distinct reflections will appear on the glass surface. Based on this phenomenon, we propose NFGlassNet, a novel method for glass surface detection that leverages the reflection dynamics present in flash/no-flash imagery. Specifically, we propose a Reflection Contrast Mining Module (RCMM) for extracting reflections, and a Reflection Guided Attention Module (RGAM) for fusing features from reflection and glass surface for accurate glass surface detection. For learning our network, we also construct a dataset consisting of 3.3K no-flash and flash image pairs captured from various scenes with corresponding ground truth annotations. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. Our code, model, and dataset will be available upon acceptance of the manuscript.

翻译：玻璃表面在日常生活中无处不在，通常呈现无色、透明且缺乏显著特征。这些特性使得玻璃表面检测成为一项具有挑战性的计算机视觉任务。现有的玻璃表面检测方法通常依赖边界线索（如窗框和门框）或反射线索来定位玻璃表面，但未能充分利用玻璃本身的固有属性以实现精确定位。我们观察到，在大多数现实场景中，玻璃表面前方的光照强度与其后方存在差异，这导致玻璃表面可见的反射发生变化。具体而言，当站在玻璃较亮一侧并向较暗一侧使用闪光时，玻璃表面现有的反射往往会消失；反之，当站在较暗一侧并向较亮一侧使用闪光时，玻璃表面会出现明显的反射。基于这一现象，我们提出了NFGlassNet，一种利用闪光/无闪光图像中反射动态特性的新型玻璃表面检测方法。具体来说，我们设计了一个反射对比挖掘模块（RCMM）用于提取反射特征，以及一个反射引导注意力模块（RGAM）用于融合反射与玻璃表面特征以实现精确检测。为训练我们的网络，我们还构建了一个包含3.3K对无闪光与闪光图像的数据集，这些图像采集自多种场景并配有相应的真实标注。大量实验表明，我们的方法优于现有最先进技术。我们的代码、模型和数据集将在论文被接受后公开。