Abstract
In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.
Highlights
Traditional methods of tunnel inspection are based on the subjective criteria of a human operator that performs local visual inspection and sensing equipment that usually requires installation and contact with the tunnel surface
The management of a large amount of information provided by the image sensors on the entire surface of the tunnel does not allow the manual evaluation of data, so new methods for the automated processing of this data have been developed
The images of the tunnel surface obtained by using this system are processed through the crack detection algorithms
Summary
Traditional methods of tunnel inspection are based on the subjective criteria of a human operator that performs local visual inspection and sensing equipment that usually requires installation and contact with the tunnel surface (plaster indicators, crackmeters, etc.). The automatic acquisition and processing of images emerges as the only practical solution To this end, the development of image processing algorithms capable of detecting the presence of defects in the surface of the tunnel is necessary. The management of a large amount of information provided by the image sensors on the entire surface of the tunnel does not allow the manual evaluation of data, so new methods for the automated processing of this data have been developed. The proposed methods allow for the inspecting and detecting of fissures and cracks in the surface of the tunnel. This type of defect is one of the most usual, and one of the most difficult to detect
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