Surface Defect Detection Technology for Solar Panels Based on Machine Vision

This study aims to address the challenges faced by traditional spatial defect detection algorithms when dealing with solar panels with complex grid line texture details. An innovative surface defect detection algorithm based on frequency domain filtering is proposed. To effectively capture and analyze surface defects of solar panels, a high-precision machine vision image acquisition system was first designed, which can stably obtain solar panel images containing subtle defects. Subsequently, median filtering is applied to the collected images to remove noise, and the emphasis enhancement technique is applied to improve image contrast. The image is then split into three color channels, R, G, and B, for optimization processing based on specific features. In the core part of the algorithm, the preprocessed R channel image is selected as the analysis object, and the Fast Fourier Transform (FFT) is used to convert it to the frequency domain, forming a spectral image. Through a carefully designed frequency domain filtering strategy, high-frequency components and isolated noise representing grid line texture details in the spectrum are effectively filtered out, thereby preserving key information related to defects. Subsequently, the processed spectral image was transformed back into the spatial domain using Inverse Fast Fourier Transform (IFFT), resulting in an image with significantly reduced texture interference. Finally, the Otsu automatic threshold segmentation method was applied to divide the image into regions, and combined with morphological processing to further refine the segmentation results, achieving accurate recognition and extraction of surface defects on solar panels. Experimental verification shows that the algorithm not only has excellent robustness and can accurately identify defects in complex texture backgrounds, but also exhibits high detection speed and accuracy, providing a detection method for quality control in the solar panel manufacturing industry.