Weeds detection in UAV imagery using SLIC and the hough transform

M. D. Bah, A. Hafiane, R. Canals
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引用次数: 43

Abstract

Traditional weeds controlling tended to be spraying herbicides in all the fields. Such method not only requires huge quantities of herbicides but impact environment and humans health. In this paper, we propose a new method of crop/weeds discrimination using imagery provided by an unmanned aerial vehicle (UAV). This method is based on the vegetation skeleton, the Hough transform and the spatial relationship of superpixels created by the simple linear iterative clustering (SLIC). The combination of the spatial relationship of superpixels and their positions in the detected crop lines allows to detect intraline weeds. Our method shows its robustness in presence of weed patches close to crop lines as well as for the detection of crop lines as for weed detection.
基于SLIC和hough变换的无人机图像杂草检测
传统的杂草防治往往是在所有的田间喷洒除草剂。这种方法不仅需要大量的除草剂,而且对环境和人类健康造成影响。本文提出了一种利用无人机(UAV)提供的图像进行作物/杂草识别的新方法。该方法基于植被骨架、Hough变换和简单线性迭代聚类(SLIC)生成的超像素空间关系。结合超像素的空间关系及其在检测作物线中的位置,可以检测到线内杂草。我们的方法在靠近作物株系的杂草斑块存在以及作物株系检测和杂草检测方面显示出其鲁棒性。
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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