  Solar cell electroluminescence (EL) defect segmentation is an interesting and
challenging topic. Many methods have been proposed for EL defect detection, but
these methods are still unsatisfactory due to the diversity of the defect and
background. In this paper, we provide a new idea of using generative
adversarial network (GAN) for defect segmentation. Firstly, the GAN-based
method removes the defect region in the input defective image to get a
defect-free image, while keeping the background almost unchanged. Then, the
subtracted image is obtained by making difference between the defective input
image with the generated defect-free image. Finally, the defect region can be
segmented through thresholding the subtracted image. To keep the background
unchanged before and after image generation, we propose a novel strong identity
GAN (SIGAN), which adopts a novel strong identity loss to constraint the
background consistency. The SIGAN can be used not only for defect segmentation,
but also small-samples defective dataset augmentation. Moreover, we release a
new solar cell EL image dataset named as EL-2019, which includes three types of
images: crack, finger interruption and defect-free. Experiments on EL-2019
dataset show that the proposed method achieves 90.34% F-score, which
outperforms many state-of-the-art methods in terms of solar cell defects
segmentation results.
