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Research on construction site dust detection based on prior knowledge MinMax k-Means

机译：基于先前知识的施工现场粉尘检测研究Minmax K-Mean

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With the rapid development of urbanization, promoting the process of industrialization has become the best breakthrough to accelerate economic development. The number of construction projects under construction is increasing gradually. In recent years, people are paying more and more attention to the impact of atmospheric particulate matter on the environment and human health. Construction dust is a by-product of open-air construction activities, which does great harm to the ecological environment and human health. It contributes 13.1 % [1]to urban PM2.5 pollution and is also one of the main sources of atmospheric pollutant PM10[2]. In order to timely detect construction site dust and improve the ability of government supervision departments to monitor construction dust pollution, a construction site dust detection method based on prior knowledge Minmax K-means clustering algorithm was proposed. In the process of clustering, the weight which is proportional to the variance in the cluster can be automatically corrected, and the priori knowledge is introduced to deal with the problem that the clustering results are sensitive to the initial position of the clustering center. In addition, the preprocessing adopts the method that the mean value of image blocks with dust is larger than that without dust, and scans the mean value matrix from vertical and horizontal directions to judge whether the image blocks have dust.
机译：随着城市化的快速发展，促进产业化进程已成为加快经济发展的最佳突破。建设的建设项目数量逐渐增加。近年来，人们越来越多地关注大气颗粒物质对环境和人类健康的影响。建筑粉尘是露天建设活动的副产品，对生态环境和人类健康有很大危害。它贡献了13.1％[1]到城市PM2.5污染，也是大气污染物PM10 [2]的主要来源之一。为了及时检测施工现场粉尘，提高政府监督部门监测施工粉尘污染的能力，提出了一种基于先前知识Minmax K-Meast聚类算法的建造场所粉尘检测方法。在聚类过程中，可以自动纠正与群集中的方差成比例的权重，并引入先验知识来处理聚类结果对聚类中心的初始位置敏感的问题。另外，预处理采用的方法采用灰尘的图像块的平均值大于没有灰尘的图像块的平均值，并且扫描平均值矩阵从垂直和水平方向扫描以判断图像块是否具有灰尘。

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