Fast Crack Detection Using Convolutional Neural Network

Autor:	Yang, Jiesheng, Lin, Fangzheng, Xiang, Yusheng, Katranuschkov, Peter, Scherer, Raimar J.
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary for the concrete benchmark dataset. As the results show, our model is demonstrated as an effective tool for industry use. Comment: 10 pages, 11 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.10892 Zobrazit plný text záznamu View this record from Arxiv