This paper describes the multilayer level set method for identification of surface defects within a material. This method relies upon the examination of temperature variations within the material. Even though several image processing techniques have used thermal images for detection of surface defects. Detecting and locating surface defects from thermal images is difficult. Mumford and Shah proposed to divide an image in a set of homogeneous sub-regions such that the energy contained in the image can be minimized. Based on this minimization of the energy, the multilayer level set method implicitly presents the regional boundaries as several nested level lines. By increasing iterations and pre-selected level values, these lines evolve close to the level boundaries based on the energy minimization. In this paper, two kinds of tests are employed to evaluate the performance of the algorithm: the first is that the artificial defects are buried behind the tiles; the second that the artificial defectors are buried behind and near the surface of a structure covered with carbon fiber reinforced plastic (CFRP). With a set of halogen lights used to heat the structure, a thermal camera with temperature resolution 0.1 deg C is employed to record the temperature changes. The experimental results show that, according to the predefined level values, the multilayer level set method can successfully detect regional boundaries of the buried defects by identifying temperature changes within their neighborhoods using infrared thermal images.
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