确定地形特征颜色是发展迷彩的手段

Serhii Tsybulia
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引用次数: 0

摘要

颜色和图案是迷彩视觉特征的组成部分。考虑到俄乌战争期间军事行动的经验,这些手段可以通过消除这些军事设施的特征暴露迹象并将其隐藏在植被、沙漠草原、积雪和城市化地区的背景中,大大提高人员、武器和军事装备的生存能力和安全性。本文研究了伪装隐蔽手段设计的第一阶段——区域特征颜色的识别。提出了使用与无监督机器学习方法相关的聚类来进行特征颜色的识别。簇的数量决定了将在掩蔽表面上显示的颜色的数量。确定了以数字JPEG格式存储的地形图像进行分析是可取的,颜色以RGB加色模型表示。在进行研究时,我们使用了k-means这样的图像分析聚类方法,它比其他聚类方法具有易于实现、不占用资源和足够的计算速度的优点。其他聚类方法,如分层或基于密度的聚类方法,尚未被证明适合图像聚类。并与常用的聚类方法c-means、DBSCAN、OPTICS、团聚聚类、光谱双聚类等进行了比较。对选择聚类数量的各种算法进行了测试,根据实验结果,选择“肘部”方法为最优方法。数学算法来自开源,它们的实现是使用Python编程语言的机器学习通用软件库进行的。这项工作的结果使得选择数学算法来确定伪装的颜色数量成为可能。这将允许分析乌克兰所有自然地带的地形,并为乌克兰武装部队设计有效的伪装覆盖物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of the characteristic colors of the terrain in the development of camouflage means
Color and pattern are integral parts of the visual characteristics of camouflage. These means, taking into account the experience of military operations during the Russian-Ukrainian war, can significantly increase the survivability and safety of personnel, weapons and military equipment, by eliminating the characteristic unmasking signs of these military facilities and hiding them on vegetative, desert-steppe, snowy and urbanized areas background. The paper considers the first stage in the design of camouflage means of concealment - the identification of the characteristic colors of the area. The identification of characteristic colors is proposed to be carried out using clustering related to unsupervised machine learning methods. The number of clusters determines the number of colors that will be displayed on the masking surface. It was determined that it is advisable to analyze terrain images stored in the digital JPEG format, and the colors are represented in the RGB additive color model. When conducting research, such a clustering method for image analysis as k-means was used, which has an advantage over other clustering methods in ease of implementation, unpretentiousness in resources and sufficient computational speed. Other clustering methods, such as hierarchical or density-based, have not proven to be suitable for image clustering. The comparison was made with the most common clustering methods: c-means, DBSCAN, OPTICS, agglomerative, spectral biclustering, etc. Various algorithmic approaches to choosing the number of clusters were tested, according to the results of the experiments, the “elbow” method was chosen as the most optimal one. Mathematical algorithms were taken from open sources, their implementation was carried out using common software libraries for machine learning of the Python programming language. The results of the work made it possible to choose mathematical algorithms for determining the number of colors of camouflage means of concealment. This will allow to analyze the terrain of all natural zones of Ukraine and design effective camouflage coverings for the Armed Forces of Ukraine.
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