Robust Image-Based Surface Crack Detection Using Range Data

Abstract

Image-based automated crack detection techniques have been developed and applied extensively during the past decade. However, issues existing in this type of methodology have not yet been fully addressed. Early studies adopted image intensity data for crack detection, assuming the crack pixels are darker than their surrounding pixels. However, when the intensity image suffers from shadows, blemishes, or poor contrast between cracks and surrounding surface, this assumption is compromised. Another issue exists in the image preprocessing stage. Traditional methods, such as median filtering and surface fitting, are used for background correction and crack enhancement purposes. Because most of these preprocessing techniques require subjective parameters such as kernel size of the filter, their performance can be influenced by different images and operating personnel. Moreover, conventional crack detection methods such as edge detection usually do not provide an enclosed crack boundary. This leads to difficulties in extracting individual cracks and estimating the associated cracking properties. To address these issues, an automated crack-detection method that utilizes frequency domain filtering and contouring analysis on three dimensional (3D) laser image range data is proposed. In the image preprocessing stage, frequency domain filtering techniques are applied to remove surface variations (or oscillations), image noises, and grooved patterns. In the proposed methodology, the relationship between the width of a crack and the cutoff frequency of the image filters is derived. The filter parameters are designed based on the physical characteristics (e.g., crack width, groove spacing, and orientation), thus having less subjectivity. A systematic crack detection methodology based on contouring analysis and a set of logics and criteria is proposed to extract enclosed crack boundary and provide accurate detection results under noise contamination and surface variations. Experimental study of crack detection on bridge deck surfaces is performed. The crack detection results are examined through a precision-recall analysis, which successfully demonstrates the effectiveness and robustness of this methodology.