Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-03 Quang Trung, Da Nang, Vietnam.
Comput Intell Neurosci. 2018 Oct 1;2018:1312787. doi: 10.1155/2018/1312787. eCollection 2018.
To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.
为了提高沥青路面状况定期调查的效率，本研究提出了一种自动化路面裂缝模式分类的智能方法。新方法依赖于图像处理技术和计算智能算法。拉普拉斯金字塔和投影积分的图像处理技术用于从数字图像中提取数值特征。最小二乘支持向量机（LSSVM）和差分花粉授粉（DFP）是两种计算智能算法，用于根据提取的特征构建裂缝分类模型。LSSVM 用于数据分类。此外，LSSVM 的模型构建阶段需要适当设置正则化和核函数参数。本研究依靠 DFP 来微调 LSSVM 的这两个参数。已经收集了一个由 500 个图像样本和五个类别标签（鳄鱼纹裂缝、对角裂缝、纵向裂缝、无裂缝和横向裂缝）组成的数据集，用于训练和验证所建立的方法。实验结果表明，拉普拉斯金字塔确实有助于增强路面图像并揭示裂缝模式。此外，LSSVM 和 DFP 的混合，称为 DFP-LSSVM，与第 4 级的拉普拉斯金字塔一起使用，可以帮助我们实现最高的分类准确率 93.04%。因此，DFP-LSSVM 的新混合方法是辅助运输机构进行路面状况调查的有前途的工具。
