Automated Crack Detection and a Web Tool Using Image Processing Techniques in Concrete Structures
Source abstract
Abstract
Cracks indicates the real time deformity in concrete structures. It is characterized as discontinuity in terms of shape and size of the concrete structures. To ensure the structural health and safety, crack detection is an important task. The traditional methods of crack detection include visual introspection, ultrasonic and hand-held testing of crack. These methods require a high human intervention along with an experienced and skilled inspector. Moreover, these methods are subjective and time-consuming process which fails to identify the crack of the complex concrete structures properly. To overcome these issues, a GrabCut with improved Sobel has been proposed for automatic crack detection from the concrete structures. The proposed method works as a two-step model where cracks regions are segmented in the first step and a precise crack assessment is performed in the second step. Furthermore, to improve the efficacy of Sobel, the mask is modified with the aid of local variance of the image instead of using conventional mask of the filter. For the experimentation study, the images of self-prepared concrete sample have been acquired. The effectiveness of the proposed method has been compared with respect to various pre-existing methods like Sobel, Prewitt, Robert, LoG, Zero Cross, and Canny. The comparative qualitative result exhibits that the proposed method surpasses the outcomes of the other pre-existing methods. Additionally, for easy implementation and application point of view a web tool of the proposed method has been developed. The web tool can be utilised by the civil infrastructure maintenance agency and construction engineers in the task of structure maintenance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Concrete Corrosion and Durability · Asphalt Pavement Performance Evaluation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
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