Thin-walled turbine blades with complex features are a critical part of an aviation engine, and a small change in their geometric shape can erode the performance of the aviation engine. Inspecting the blade with an optical device is a promising technique. One key task involved is the calibration of the optical sensor with the rotating platform. This paper presents a novel calibration method for the optical inspection of the blade. Three target spheres are measured by a high-precision laser tracker and an optical sensor. The positions of the sphere centers are used to build a coordinate system and an approximated plane. Following that, the rotation axis and the rotation center of the rotating platform are easily calculated. According to a direction vector from the measured stripe, the transformation parameters between the optical sensor and rotating platform are further calculated. This calibration method is simple to carry out, and it guarantees that all the measured points are represented in the same coordinate system for subsequent parameter extraction and profile error evaluation of the blade surface. The experiments demonstrate the feasibility of the proposed method, and it found that the measurement error after calibration is within 0.02 mm.
Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.
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