ABSTRACT

Cracks in the asphalt are the first and most common deterioration type of roads that generally threaten the safety of roads and highways. In recent years, automated inspection has been considered due to the high cost and error of manual methods. For this purpose, different machine learning techniques have been developed. In this study, an integrated model is proposed, which involves the following steps: image segmentation, noise reduction, feature extraction, and crack classification. In the first two steps, heuristic algorithms are proposed, and then in the third step, the Hough transform technique and the heuristic equations are used to extract the main features of cracks. In the fourth step, six different classification models, including neural network, SVM, decision tree, KNN, Bagged Trees, and a proposed hybrid model, are implemented. Experimental results show that the proposed hybrid model can achieve more accurate results with 93.86% overall accuracy.

摘要

沥青裂缝是第一种也是最常见的道路劣化类型，通常会威胁道路和高速公路的安全。近年来，由于人工方法的高成本和错误，已考虑自动化检查。为此，已经开发了不同的机器学习技术。在这项研究中，提出了一个集成模型，包括以下步骤：图像分割、降噪、特征提取和裂缝分类。在前两步中，提出了启发式算法，然后在第三步中，利用霍夫变换技术和启发式方程来提取裂缝的主要特征。第四步，实现了六种不同的分类模型，包括神经网络、SVM、决策树、KNN、袋装树和提出的混合模型。