Development of an Automatic Detector of Cracks in Concrete Using Machine Learning. (2017)

• Record Type: Journal Article
• Authors: Yokoyama, Suguru; Matsumoto, Takashi
• Is Part Of: Procedia engineering. Volume 171(2017)
• Page Start: 1250
• Page End: 1255
• Publication Date: 2017
• Subjects: machine learning -- deep learning -- cracked concrete -- automatic detection; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.01.418
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: This study is to develop a detector that automatically detects cracks from the photographs of concrete structures, using convolution neural network which is a kind of deep learning. Firstly, photographs of concrete were collected for the learning data. Secondly, pictures of cracked part, chalk letter part, joint part, surface part and others part were produced from these photographs for the dataset. Thirdly, classifier to classify into these 5 class from pictures was created using the dataset and convolution neural network. Finally, the automatic detector was produced using this classifier.