Automatic Identification Of Construction Dust Based On Improved K-Means Algorithm

Abstract

Abstract At present, the construction dust has caused great losses to people's healthy life and national economic development. In order to solve the shortcomings of the existing real-time sensor network detection of construction dust such as poor accuracy, this paper proposes an automatic identification of construction dust based on computer vision and improved K-Means clustering algorithm. We extract the saturation of HSV color model of each image to form a text data set. We determine the median value of the data set as the initial centroid of clustering, reduce the number of iterations and get the global optimal solution. Mahalanobis distance is used as similarity measure to cluster, which reduces the difference between different feature measures, improves the accuracy, and realizes the automatic recognition of construction dust based on computer vision. For the improved K-Means algorithm, the precision, recall rate and harmonic mean value are used to analyze the clustering results. The experimental results show that the improved K-Means algorithm has good robustness and high accuracy, and the automatic recognition rate can reach 89.33%.