# Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

arXiv: https://arxiv.org/abs/2106.10962 · HTML: https://mlsanity.com/paper/2106.10962/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://mlsanity.com/paper/1901.09221/) (2019)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm](https://mlsanity.com/paper/2001.11248/) (2020)

## Built on by (most cited first, 5 of 5)

- [A scalable framework for annotating photovoltaic cell defects in electroluminescence images](https://mlsanity.com/paper/2212.07768/) (2022)
- [Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability](https://mlsanity.com/paper/2411.19514/) (2024)
- [Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells](https://mlsanity.com/paper/2208.05994/) (2022)
- [MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells](https://mlsanity.com/paper/2603.13337/) (2026)
- [STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation](https://mlsanity.com/paper/2608.01714/) (2026)

## Abstract (arXiv)

In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution electroluminescence (EL) imaging. The difficulty of acquiring such images has limited the availability of data. For this work, multiple data resources and augmentation techniques have been used to surpass this limitation. Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised segmentation (autoencoder). The modular nature of the pipeline allows to upgrade the deep learning models to the further improvements in the state-of-the-art and also extend the pipeline towards new functionalities.