鲁棒眩光检测：回顾，分析和数据集发布
Robust Glare Detection: Review, Analysis, and Dataset Release
摘要：
太阳眩光广泛存在于无人驾驶地面和飞行器在户外环境中拍摄的图像中。图像中这些伪影的存在将导致错误的特征提取和自治系统的失败。人类一旦观察到眩光（特别是在驾驶时），就会试图调整自己的视野，而这种行为是下一代自动驾驶汽车的基本要求。眩光的来源并不限于太阳，在夜间和室内环境中拍摄的图像中可以看到眩光，这是由于存在不同的光源;反射表面也会影响这种伪影的产生。眩光的视觉特征在不同相机拍摄的图像上是不同的，并且取决于几个因素，例如相机的快门速度和曝光水平。因此，它是具有挑战性的，引入一个通用的-鲁棒和准确的-算法眩光检测，可以在各种捕获的图像表现良好。本研究旨在介绍第一个眩光检测数据集，其中包括由不同相机捕获的图像。此外，多个图像表示和它们的组合在眩光检测的效果检查使用所提出的深度网络架构。发布的数据集可在https://github.com/maesfahani/glaredetection上获得
英文摘要：
Sun Glare widely exists in the images captured by unmanned ground and aerial vehicles performing in outdoor environments. The existence of such artifacts in images will result in wrong feature extraction and failure of autonomous systems. Humans will try to adapt their view once they observe a glare (especially when driving), and this behavior is an essential requirement for the next generation of autonomous vehicles. The source of glare is not limited to the sun, and glare can be seen in the images captured during the nighttime and in indoor environments, which is due to the presence of different light sources; reflective surfaces also influence the generation of such artifacts. The glare's visual characteristics are different on images captured by various cameras and depend on several factors such as the camera's shutter speed and exposure level. Hence, it is challenging to introduce a general - robust and accurate - algorithm for glare detection that can perform well in various captured images. This research aims to introduce the first dataset for glare detection, which includes images captured by different cameras. Besides, the effect of multiple image representations and their combination in glare detection is examined using the proposed deep network architecture. The released dataset is available at https://github.com/maesfahani/glaredetection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
