通过颜色特征识别航空图像中的光伏板

Daniele Marletta, Alessandro Midolo, E. Tramontana
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引用次数: 0

摘要

从图像中检测光伏板是一个重要的领域,因为它通过评估社区的能源自主水平来利用预测和规划绿色能源生产的可能性。许多现有的光伏板检测方法都是基于机器学习的;然而,它们需要大量带注释的数据集和大量的训练,并且结果并不总是准确或可解释的。本文提出了一种自动检测光伏板的方法,该方法符合根据分析图像中给定的光照条件提取的适当形成的显着颜色范围。颜色的显著范围是由一个注释的图像数据集自动形成的,由最常见的面板颜色组成,不同于周围部分的颜色。然后,通过分析面板颜色、计算像素密度和可比较的光线水平,这些颜色被用来检测其他图像中的面板。我们的方法产生的结果比以前文献中的其他方法更精确,因为我们的工具准确地揭示了面板的轮廓,而不管它们的形状或周围物体和环境的颜色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours
The detection of photovoltaic panels from images is an important field, as it leverages the possibility of forecasting and planning green energy production by assessing the level of energy autonomy for communities. Many existing approaches for detecting photovoltaic panels are based on machine learning; however, they require large annotated datasets and extensive training, and the results are not always accurate or explainable. This paper proposes an automatic approach that can detect photovoltaic panels conforming to a properly formed significant range of colours extracted according to the given conditions of light exposure in the analysed images. The significant range of colours was automatically formed from an annotated dataset of images, and consisted of the most frequent panel colours differing from the colours of surrounding parts. Such colours were then used to detect panels in other images by analysing panel colours and reckoning the pixel density and comparable levels of light. The results produced by our approach were more precise than others in the previous literature, as our tool accurately reveals the contours of panels notwithstanding their shape or the colours of surrounding objects and the environment.
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