  Photovoltaic (PV) systems allow us to tap into all abundant solar energy,
however they require regular maintenance for high efficiency and to prevent
degradation. Traditional manual health check, using Electroluminescence (EL)
imaging, is expensive and logistically challenging which makes automated defect
detection essential. Current automation approaches require extensive manual
expert labeling, which is time-consuming, expensive, and prone to errors. We
propose PV-S3 (Photovoltaic-Semi Supervised Segmentation), a Semi-Supervised
Learning approach for semantic segmentation of defects in EL images that
reduces reliance on extensive labeling. PV-S3 is a Deep learning model trained
using a few labeled images along with numerous unlabeled images. We evaluate
PV-S3 on multiple datasets and demonstrate its effectiveness and adaptability.
With merely 20% labeled samples, we achieve an absolute improvement of 9.7% in
IoU, 13.5% in Precision, 29.15% in Recall, and 20.42% in F1-Score over prior
state-of-the-art supervised method (which uses 100% labeled samples) on UCF-EL
dataset (largest dataset available for semantic segmentation of EL
images)showing improvement in performance while reducing the annotation costs
by 80%.
