  In this manuscript, a pipeline to develop an inspection system for defect
detection of solar cells is proposed. The pipeline is divided into two phases:
In the first phase, a Generative Adversarial Network (GAN) employed in the
medical domain for anomaly detection is adapted for inspection improving the
detection rate and reducing the processing rates. This initial approach allows
obtaining a model that does not require defective samples for training and can
start detecting and location anomaly cells from the very beginning of a new
production line. Then, in a second stage, as defective samples arise, they will
be automatically labeled at pixel-level with the trained model and employed for
supervised training of a second model. The experimental results show that the
use of such automatically generated labels can improve the detection rates with
respect to the anomaly detection model and the model trained on manual labels
made by experts.
