Research on defect detection method for high-reflective-metal surface based on high dynamic range imaging

Abstract

The high dynamic range imaging (HDRI) technology can realistically reproduce the scene and acquire more image information for analysis and processing. In this paper, a defect detection method based on HDRI is proposed to solve the visual inspection problem of industrial parts with high-reflective surface. Firstly, a fast calibration method is put forward to increase the calibration speed of the camera response function (CRF). Then, a novel algorithm named adaptive tone mapping based on color correction model in gradient domain is designed to adaptively adjust image chromaticity information. Finally, adaptive threshold segmentation and Haar-like feature extraction algorithm are used to detect surface defects in synthesized high dynamic range (HDR) images. The experimental results show that the HDR images processed by our algorithm can meet the detection requirements for high-reflective-metal surface. The number of sampling points required by our fast calibration algorithm of CRF is 1/10 times than that of the traditional algorithm, and the running time in the program is about 1/4 times than that of the traditional algorithm. What’s more, our improved local tone mapping algorithm is obviously improved in information entropy, contrast and average gradient.