# Micro-Fracture Detection in Photovoltaic Cells with Hardware-Constrained Devices and Computer Vision

arXiv: https://arxiv.org/abs/2403.05694 · HTML: https://mlsanity.com/paper/2403.05694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis](https://mlsanity.com/paper/1809.05989/) (2018)
- [Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images](https://mlsanity.com/paper/1806.06530/) (2018)
- [Continual Learning on the Edge with TensorFlow Lite](https://mlsanity.com/paper/2105.01946/) (2021)
- [On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation](https://mlsanity.com/paper/2107.02466/) (2021)
- [Deep Learning-based Pipeline for Module Power Prediction from EL Measurements](https://mlsanity.com/paper/2009.14712/) (2020)
- [CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection](https://mlsanity.com/paper/2204.11766/) (2022)

## Abstract (arXiv)

Solar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e. semiconducting materials that convert effectively the solar radiation into electricity. However, crystalline silicon is fragile and vulnerable to cracking over time or in predictive maintenance tasks, which can lead to electric isolation of parts of the solar cell and even failure, thus affecting the panel performance and reducing electricity generation. This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alaert of a potential failure of the photovoltaic system by using computer vision techniques. Three scenarios are defined where these techniques will bring value. In scenario A, images are taken manually and the system detecting failures in the solar cells is not subject to any computationa constraints. In scenario B, an Edge device is placed near the solar farm, able to make inferences. Finally, in scenario C, a small microcontroller is placed in a drone flying over the solar farm and making inferences about the solar cells' states. Three different architectures are found the most suitable solutions, one for each scenario, namely the InceptionV3 model, an EfficientNetB0 model shrunk into full integer quantization, and a customized CNN architechture built with VGG16 blocks.