Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique
المؤلفون المشاركون
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-30
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Crack detection is important for the inspection and evaluation during the maintenance of concrete structures.
However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc.
To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN).
A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images.
Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process.
The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation.
The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice.
The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Shengyuan& Zhao, Xue-Feng. 2019. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1116757
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Shengyuan& Zhao, Xue-Feng. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1116757
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Shengyuan& Zhao, Xue-Feng. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Advances in Civil Engineering. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1116757
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1116757
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