Iterating Tensor Voting: A Perceptual Grouping Approach for Crack Detection on EL Images

Abstract

The surface of a multicrystal solar cell shows multiple crystal grains of random shapes and sizes. It creates an inhomogeneous texture in the surface, which brings great difficulty to automatic crack detection of polycrystalline solar surface. As a perceptual grouping approach, tensor voting can extract curvilinear structures such as lines and curves from noisy, binary data in 2-D or 3-D, without invoking specific object or model. However, traditional tensor voting can be susceptible to the gap problem and structural noise. To address the problems mentioned above, a new iterative tensor voting algorithm is presented, which efficacy bases on iterative refinements of the curvilinear structures. By combining the proximity and continuity of Gestalt principles, in each iteration step, a new decay function is redefined according to the difference of angle between the voter and receiver to rebuild the voting field, which makes the points that lie on curvilinear structures vote more information (a bigger tensor) to the ones with the same attribute. The proposed method can solve the gap problem and is robust to structural noises. The experimental results show that the proposed method can detect crack on the inhomogeneous textured surface and achieve an average detection rate of 95.2% on the industrial data set. Note to Practitioners-Automatic vision-based defect detection on the solar cell is difficult due to inhomogeneous texture and low contrast between defects and background in the surface. In order to solve these problems, by combining the proximity and continuity of Gestalt principles, this article proposed a new iterative tensor voting algorithm which can refine the curvilinear structures with iterations. Experiments have shown that the proposed method can detect crack under the interference of inhomogeneous texture and complex background.