Zhao Ziting, Liu Tong, Zhao Xudong
College of Civil Engineering, Northeast Forestry University, Harbin 150040, China.
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
Comput Intell Neurosci. 2021 Mar 6;2021:5538573. doi: 10.1155/2021/5538573. eCollection 2021.
Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.
机器学习在计算智能中发挥着重要作用，并且已在许多工程领域中广泛使用。混凝土浇筑后，混凝土表面经常出现的表面孔洞或气孔使得相应的人工检测既耗时、成本高、劳动强度大，而且结果不一致。为了更好地检测混凝土表面，需要对混凝土气孔进行自动分类。本文提出了一种用于追求特征可解释性的变量选择策略，以及一种为提高气孔分类精度而设计的自动集成分类方法。在混凝土表面图像中提取了源自Gabor滤波器和灰度游程长度的纹理特征。根据提出的累积投票策略选择可解释变量，这些变量也是特征的组成部分。提供了一个自动分配其基础分类器的集成分类器，以检测图像中是否存在表面孔洞。对1000个图像样本的实验结果表明了我们方法的有效性，具有可比的预测精度且模型可解释。
