Joint Super-Resolution and Rectification for Solar Cell Inspection

TL;DR

This paper introduces a combined approach for super-resolution and rectification in solar module inspection, improving defect detection accuracy and automated crack segmentation by integrating these processes into a single framework.

Contribution

It proposes a novel fusion of multi-frame super-resolution with rectification, enhancing stability and accuracy in solar module defect analysis.

Findings

MFSR improves defect recognition by human experts.

The method outperforms state-of-the-art in automated crack segmentation.

Achieves 3x better results than bicubic upsampling.

Abstract

Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this pre-processing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution (HR) pixels on the rectified module image becomes uniform w. r. t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state of the art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3x better than bicubic upsampling and 2x better than the state of the art for automated inspection.