# Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network

arXiv: https://arxiv.org/abs/2103.03518 · HTML: https://mlsanity.com/paper/2103.03518/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Anomaly Detection using Deep Learning based Image Completion](https://mlsanity.com/paper/1811.06861/) (2018)
- [Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm](https://mlsanity.com/paper/2001.11248/) (2020)
- [Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network](https://mlsanity.com/paper/1812.06220/) (2018)

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [Generative AI in Industrial Machine Vision -- A Review](https://mlsanity.com/paper/2408.10775/) (2024)
- [Few-shot incremental learning in the context of solar cell quality inspection](https://mlsanity.com/paper/2207.00693/) (2022)
- [ForecastGAN: A Decomposition-Based Adversarial Framework for Multi-Horizon Time Series Forecasting](https://mlsanity.com/paper/2511.04445/) (2025)

## Abstract (arXiv)

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patternsand the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localizationof anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without anymanual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automaticallygenerated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types offaults. The experimental results using 1873 EL images of monocrystalline cells show that (a) the anomaly detection scheme can beused to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order totrain a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels arecomparable to the ones obtained from the models trained with manual labels.