A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

Applied Sciences

Stephen So¹  Hong Guan¹  Younes Hamishebahar²  Jun Jo² 

[1] School of Engineering and Built Environment, Gold Coast Campus, Griffith University, Southport, QLD 4222, Australia;School of Information and Communication Technology, Gold Coast Campus, Griffith University, Southport, QLD 4222, Australia;

关键词: structural health monitoring;    crack detection;    deep learning;    image classification;    object recognition;    semantic segmentation;   

DOI  :  10.3390/app12031374

来源: DOAJ

【 摘 要 】

The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area.

【 授权许可】

Unknown