Deep Learning Approach for Crack Detection in Solar Panels using Convolutional Neural Networks

Abstract

The utilization of solar panels, which are effective power sources for producing electrical energy, allows for the widespread application of solar energy,
a clean and renewable substitute for conventional fuels. However, there is
a chance that manufacturing, delivery, and installation errors will lower the
effectiveness of power generation. Moreover, detecting surface cracks on solar
panels is crucial to ensure the durability and effectiveness of photovoltaic systems. By instructing the network to find flaws in photos of solar panels, convolutional neural networks provide a practical way to address this problem.
During training, the CNN gains the ability to distinguish between patterns that
are normal and those that indicate a fault. After being trained, the network can
accurately and effectively detect fractures in recent data.