基于图像增强和阈值法的道路不规则检测

C. Șorândaru, I. Stanciu
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引用次数: 2

摘要

在过去的几年里,无人驾驶汽车已经取得了重大进展。现在的汽车通常都有摄像头。它们能够在没有信号的情况下提醒驾驶员变道,跟踪车道标记,检测雾、雪、雨、太阳刺眼等天气现象,以及自动视频巡航控制等。所有这些行为都增加了驾驶的乐趣。检测道路上的坑洼和/或碎片,并评估它们相对于汽车车轮的位置,以便改变悬架性能,似乎是指日之事。本文介绍了一种用于路面图像处理以检测坑洼和道路缺陷的机制。如果私家车有组织地向道路服务处提交检测工作,可以减少道路服务处的开支。在一系列图像中检测凹坑可以进行跟踪和车轮撞击估计。这样,控制系统就能就此事指示汽车的悬架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Road Irregularities by Image Enhancements and Thresholding
Significant steps have been made towards driverless cars in the last couple of years. Nowadays car’s often posses cameras. They are able to alert the driver in the event of a not signalized lane change, follow lane markers, detect weather phenomena like fog, snow, rain, sun glaring, automatic video cruise control, etc. Al such behaviors enhance the driving pleasure. Detecting potholes and/or debris on the road and assessing their position relative to the car wheels in order to change the suspension properties seems right around the corner. This paper introduces a mechanism used to process the pavement images to detect potholes and or road defects. Such detection done by private cars may reduce the Road Service Department’s expense if submitted to them in an organized manner. Detecting a pothole in a succession of images allows tracking and wheel-hitting estimation. Such way, the control system is able to instruct the car’s suspension regarding this matter.
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