Research on Haze Removal for Autonomous Car

Zhi Wang, D. Watabe
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Abstract

: To improve the safety of autonomous cars, their obstacle detection capability in bad weather must be substantially improved. Haze is a major factor that degrades outdoor images. Although various dehazing schemes have been proposed, a dehazing scheme designed to improve obstacle detection capability has not been reported. Hence, we present a dehazing algorithm that enhances the safety of an autonomous car. This algorithm should be able to work in real time, even using edge computers typically installed as car electronics. Furthermore, this algorithm should work on grayscale images, as systems dependent on color images are often unaffected by environmental color changes caused by factors such as a setting sun. The empirical results showed that our grayscale image-based proposed algorithm is comparable to the results of current cutting-edge methods, and operates in real time.
自动驾驶汽车雾霾去除技术研究
为了提高自动驾驶汽车的安全性,它们在恶劣天气下的障碍物检测能力必须大幅提高。雾霾是影响室外图像质量的主要因素。虽然已经提出了各种各样的除雾方案,但一种旨在提高障碍物检测能力的除雾方案尚未被报道。因此,我们提出了一种增强自动驾驶汽车安全性的除雾算法。这种算法应该能够实时工作,甚至使用通常安装在汽车电子设备上的边缘计算机。此外,该算法应该适用于灰度图像,因为依赖于彩色图像的系统通常不受环境颜色变化(如夕阳)的影响。实验结果表明,本文提出的基于灰度图像的算法与当前前沿方法的结果相当,并且具有实时性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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