Abstract

This paper presents an enhanced and robust approach to detect and classify pavement cracks from captured images. The approach was based on three stages: pre-processing, feature extraction and classification. In pre-processing, we carried out several algorithms to compensate the impact of quality distortions during image acquisition. Then, features are retrieved from projective integrals computed on edge images. These features fed machine learning algorithms to classify the type of crack that may appear in a pavement image. The obtained results proved the relevance of our reduced features. We achieved the best successful classification rate of 93.4% using the Support Vector Machine (SVM) classifier and an accuracy of 94.7% for crack detection.

中文翻译：

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简化的基于特征的射影积分在道路裂缝检测与分类中的应用

摘要

本文提出了一种增强而强大的方法，可以从捕获的图像中检测和分类路面裂缝。该方法基于三个阶段：预处理，特征提取和分类。在预处理中，我们执行了几种算法来补偿图像采集过程中质量失真的影响。然后，从在边缘图像上计算出的投影积分中检索特征。这些功能提供了机器学习算法，可对可能出现在路面图像中的裂缝类型进行分类。获得的结果证明了我们的简化特征的相关性。使用支持向量机（SVM）分类器，我们实现了93.4％的最佳成功分类率，裂缝检测的准确度达到94.7％。