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Study on Leading Vehicle Detection at Night Based on Multisensor and Image Enhancement Method

机译:基于多传感器和图像增强法的夜间领先车辆检测研究

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摘要

Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based on multisensor to decrease rear accidents at night. Then, we use image enhancement algorithm to improve the human vision. First, by millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship between the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image coordinate in order to form the region of interesting image. And then, by using the image processing method, we can reduce interference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test vehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition at night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime driving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative study and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy the human visual habits.
机译:低能见度是晚上后事故的原因之一。本文提出了一种方法来检测基于多传感器的领先车辆,以减少晚上后遗症。然后,我们使用图像增强算法来改善人类视觉。首先,由毫米波雷达获取前面的车辆的世界坐标并建立世界坐标和图像像素坐标之间的关系的转换,我们可以将雷达目标的世界坐标转换为图像坐标,以形成该区域有趣的形象。然后,通过使用图像处理方法,我们可以减少来自外部环境的干扰。根据D-S证据理论,我们可以实现对兴趣车辆的可靠性的一般价值。实验结果表明,该方法可以有效地消除夜间照明条件的影响,准确地检测领先车辆,并确定其位置和准确定位。为了改善夜间驾驶,司机短缺愿景,减少后端事故。提出了三种算法,提高了三种算法的夜间彩色图像,通过三种算法进行了比较研究和评估。评估显示图像增强满足人类视觉习惯后的结果。

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