ABSTRACT

The pavement distress information is essential for evaluating the pavement. Automatic pavement inspection systems can be so beneficial in providing needed information on pavement conditions. The bleeding is one of the asphalt pavement distresses and directly affects the pavement skid resistance and road safety. This study aims to develop an image-based system for the comprehensive evaluation of pavement bleeding. This evaluation consists of three main parts: bleeding occurrence detection, bleeding region segmentation, and severity-based classification of bleeding regions. For implementing the proposed system, deep learning-based model and transfer learning method are used in the detection part, and wavelet transform is the main process in the segmentation part. Also, gray-level co-occurrence matrix, wavelet transform, and signal to noise ratio are used to extract image texture-based features from various levels of the bleeding severity (low, mid, and high). Then a decision tree has been made based on extracted features for severity-based classification. The proposed system can indicate the bleeding index based on distress density and severity. Results show good performance in detection, segmentation, and severity-based classification parts, on the average of performance indices with 98%, 89%, and 93%, respectively. Therefore, the proposed system provides an efficient method for comprehensive pavement bleeding evaluation.

摘要

路面损坏信息对于评估路面至关重要。自动路面检查系统在提供所需的路面状况信息方面非常有益。泌水是沥青路面病害之一，直接影响路面抗滑性能和道路安全。本研究旨在开发一种基于图像的路面渗水综合评价系统。该评估包括三个主要部分：出血发生检测、出血区域分割和基于严重程度的出血区域分类。为了实现所提出的系统，检测部分使用了基于深度学习的模型和迁移学习方法，小波变换是分割部分的主要过程。还有，灰度共生矩阵，小波变换，和信噪比用于从不同级别的出血严重程度（低、中和高）中提取基于图像纹理的特征。然后根据提取的特征制作决策树，用于基于严重性的分类。拟议的系统可以根据痛苦密度和严重程度指示出血指数。结果显示在检测、分割和基于严重性的分类部分具有良好的性能，平均性能指标分别为 98%、89% 和 93%。因此，所提出的系统为综合路面泌水评价提供了一种有效的方法。拟议的系统可以根据痛苦密度和严重程度指示出血指数。结果显示在检测、分割和基于严重性的分类部分具有良好的性能，平均性能指标分别为 98%、89% 和 93%。因此，所提出的系统为综合路面泌水评价提供了一种有效的方法。拟议的系统可以根据痛苦密度和严重程度指示出血指数。结果显示在检测、分割和基于严重性的分类部分具有良好的性能，平均性能指标分别为 98%、89% 和 93%。因此，所提出的系统为综合路面泌水评价提供了一种有效的方法。