一种用于阴影或湿路面检测和抑制的机器学习方法

Pankaj Prusty, Bibhu Prasad Mohanty
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引用次数: 0

摘要

在高级驾驶辅助系统中,路面检测是一项重要的任务。过去提出的基于强度的路面检测算法很少。然而,在检测过程中出现的问题是由于阴影或湿路面的存在。在这里,我们提出了一种利用机器学习方法检测阴影的新算法。最初,阴影是通过基于阈值的方法检测的,然后是基于窗口的方法。通过一组特征和分类器对检测到的阴影区域进行确认。将检测到的阴影或湿像素进行涂绘,得到无阴影的像素集,用于道路分类问题。该算法简单、准确,具有鲁棒性,可作为路面检测算法的一部分。
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A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces
In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.
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