Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure
Aravinda S Rao, Nguyen Tuan, Marimuthu Palaniswami, Ngo Tuan
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL | SAGE PUBLICATIONS LTD | Published : 2020
With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, w..View full abstract
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the CRC-P for Advanced Manufacturing of High Performance Building Envelope project, funded by the CRC-P program of the Department of Industry, Innovation and Science, Australia, and the Asia Pacific Research Network for Resilient and Affordable Housing (APRAH) grant, funded by the Australian Academy of Science, Australia.