经典计算机视觉与卷积神经网络在航空图像杂草映射中的比较

Paulo César Pereira Júnior, Alexandre Monteiro, Rafael Da Luz Ribeiro, A. Sobieranski, A. V. Wangenheim
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引用次数: 2

摘要

在本文中,我们提出了卷积神经网络和经典计算机视觉方法的比较,以甘蔗田航空图像杂草映射的具体精准农业问题。进行了系统的文献综述,以找出哪些计算机视觉方法正在用于这一具体问题。实现了引用最多的方法,以及四种卷积神经网络模型。所有实施的方法都使用相同的数据集进行测试,并对其结果进行定量和定性分析。所获得的结果与人类专家制作的地面事实进行了比较,以进行验证。结果表明,与经典模型相比,卷积神经网络具有更好的精度和泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images
In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.
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