Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells. (arXiv:2208.05994v1 [cs.CV])

Efficient defect detection in solar cell manufacturing is crucial for stable
green energy technology manufacturing. This paper presents a
deep-learning-based automatic detection model SeMaCNN for classification and
semantic segmentation of electroluminescent images for solar cell quality
evaluation and anomalies detection. The core of the model is an anomaly
detection algorithm based on Mahalanobis distance that can be trained in a
semi-supervised manner on imbalanced data with small number of digital
electroluminescence images with relevant defects. This is particularly valuable
for …

anomaly arxiv cells cv defects detection images segmentation solar