Classification-based Method for Wall Crack Detection System

Abstract

Abstract It is imperative to ensure that building inspectors have adequate resources and tools to conduct their inspections efficiently and effectively. Relying solely on manual labour to check for wall cracks is inconvenient and may prove inefficient and poor use of time and financial recourses. Besides, there are concerns regarding the need for skilled inspectors due to their limited accessibility and the subjective nature of their evaluations. Previously, image processing and artificial intelligence have been independently utilized to identify wall cracks and estimate their width. However, more can be done when integrating these two approaches to produce a comprehensive solution. This study presented a technique to indicate wall cracks utilizing a pre-trained Convolutional Neural Network (CNN) model called Squeezenet. Then, the following image processing can precisely estimate the width of the cracks in pixels. Based on the total models studied, 78% were successfully detected and classified into their respective crack groups. Although 22% of the remaining models were mistakenly classified, the system still managed to detect the presence of cracks in them accurately. This study only considers analyzing projected cracks categorized as minor, moderate and major. Nevertheless, the discussion does not address the translation of pixel approximations into their respective physical measurements.