Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach
المؤلف
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-01
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hoang, Nhat-Duc. 2018. Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1130583
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hoang, Nhat-Duc. Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1130583
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hoang, Nhat-Duc. Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1130583
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130583
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر