Improved Crack Detection and Recognition Based on Convolutional Neural Network. (14th October 2019)

• Record Type: Journal Article
• Authors: Chen, Keqin; Yadav, Amit; Khan, Asif; Meng, Yixin; Zhu, Kun
• Other Names: Liu Qing-Feng Guest Editor.
• Is Part Of: Modelling and simulation in engineering. Volume 2019(2019)
• Publication Date: 2019-10-14
• Subjects: Engineering -- Simulation methods -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.004
• DOI: 10.1155/2019/8796743
• Languages: English
• ISSNs: 1687-5591

Abstract

Abstract : Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.