Visual saliency–based image binarization approach for detection of surface microcracks by distributed optical fiber sensors

Early detection of defects and anomalies is important for safety and efficient management of structural elements. Brillouin scattering–based optical fiber sensors provide distributed sensing capabilities by monitoring the strain and temperature over large distances in structural elements. Their use has been limited to oil and gas explorations, mainly due to the inherent low signal-to-noise ratio in such systems, preventing detection of microcracks in structural monitoring applications. This study introduces a method based on the visual saliency approach through which the digital images acquired by the distributed strain data are employed for the detection of surface microcracks. When using this method, strain data sequences along the entire length of a structural element are sampled with a Brillouin scattering–based optical fiber sensor and then divided into a set of equal-length subsequences. A similarity measure matrix is composed based on the distributed strain data and then converted into a grayscale image. The saliency maps of the acquired grayscale images are calculated and a center-hollowed square template is defined and exploited for convolution with the binarized saliency map as a filter operator. The pixels retained after the filtering correspond to the locations of microcracks. Verification of the method was accomplished by experiments on a 15-m-long steel beam with fabricated defects.