Automatic detection of multi-crossing crack defects in multi-crystalline solar cells based on machine vision

The detection of defects in solar cells based on machine vision has become the main direction of current development, but the graphical feature extraction of micro-cracks, especially cracks with complex shapes, still faces formidable challenges due to the difficulties associated with the complex background, non-uniform texture, and poor contrast between crack defects and background. In this paper, a novel detection scheme based on machine vision to detect multi-crossing cracks for multi-crystalline solar cells was proposed. First, faced with periodic noise, we improved the filter method in the frequency domain and eliminated the background interference of fingers by filtering out the periodic noise while retaining the integrity of the crack signal. To address the anisotropy of multi-crossing cracks, we designed a special grid-shaped, convolution kernel filter to accurately extract crack features at low contrast and in the presence of a complex textured background. Finally, to address the missing features from the central region of multi-crossing cracks, we designed a method based on the orientation information of mask pattern to implement feature reconstruction for the central region of the crack. The experimental results showed that, compared to other crack detection methods, the strategy designed herein exhibited a better detection performance and stronger robustness.