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Navigation line detection based on support vector machine for automatic agriculture vehicle

机译:基于支持向量机的自动农业车辆导航线路检测

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Agriculture image segmentation plays an important role in the agriculture vehicle navigation. Robust segmentation gives a better influence on the extraction of navigation parameters. The color image was converted into gray scale image, and in order to obtain more crop row information, the average image of the gray scale one based on rectangle weight module were realized. The standard deviation of every pixel was computed too, to remain the origin crop row width. The average and the deviation value were fused and as a new input factor of support vector machine to segment image. In order to save running time of algorithm, all the operation were executed on the low resolution image obtained through wavelet analysis. The result of segmentation obviously conquered the influence of broken ridges, weeds and others high frequency disturbs. The running time is less than 0.6s when the program was written in Matlab.
机译:农业图像分割在农业车辆航行中起着重要作用。 强大的分割对导航参数的提取提供了更好的影响。 彩色图像被转换成灰度图像,并且为了获得更多的裁剪行信息,基于矩形重量模块实现灰度级的平均图像。 每个像素的标准偏差也是计算的,保持原点裁剪行宽度。 平均值和偏差值被融合,并且作为段图像的支持向量机的新输入系数。 为了节省算法的运行时间,在通过小波分析获得的低分辨率图像上执行所有操作。 分割的结果显然征服了破碎脊,杂草和其他高频扰动的影响。 当程序写入Matlab时,运行时间小于0.6s。

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