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
翻译:无人驾驶地面和空中飞行器在室外环境中拍摄的图像中广泛存在太阳灰色,这些文物在图像中的存在将造成错误的特征提取和自主系统故障。人类一旦观测到光亮(特别是驾驶时),将尝试调整其观点,这是下一代自主飞行器的一项基本要求。光亮的来源不仅限于太阳,而光亮可以在夜间和室内环境中拍摄的图像中看到,因为有不同的光源;反射表面也会影响这些文物的生成。各种相机拍摄的图像的视觉特征不同,并取决于摄像头的快门速度和暴露水平等若干因素。因此,采用一般的、稳健的和准确的光亮探测算法,可以很好地表现在各种捕获的图像中。这一研究的目的是引入第一组用于探测光亮的数据集,其中包括由不同照相机摄取的图像。此外,多个图像显示及其在光线探测中的组合的效果在各种相机中是不同的,利用提议的深网络结构来考察。在http://labarma/ 上公布的数据是可用的数据设置的。