MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202621048623 A) filed by Moresh Madhukar Mukhedkar; Ashok Patil; Sarthak Gogayan; Ganesh Pokharkar; Girijatmaj Wankhade; and Shweta Koparde on April 16, 2026, for Solar Panel Defect Detection Using Deep Learning.
Inventors include Moresh Madhukar Mukhedkar; Ashok Patil; Sarthak Gogayan; Ganesh Pokharkar; Girijatmaj Wankhade; and Shweta Koparde.
The application for the patent was published on June 12, 2026, under issue no. 24/2026.
Abstract: The present invention relates to a system and method for automated detection of defects in solar photovoltaic (PV) panels using deep learning techniques. The invention utilizes an image acquisition module to capture images of solar panels through devices such as drones, unmanned aerial vehicles (UAVs), or fixed cameras, supporting multiple imaging modalities including visible, thermal, and electroluminescence imaging. The captured images are processed through a preprocessing module that performs operations such as noise reduction, normalization, and enhancement to improve image quality. The processed images are then analyzed using a trained deep learning model, including convolutional neural networks (CNNs), transformer-based architectures, or hybrid models, to detect, classify, and localize various defects such as micro-cracks, hotspots, delamination, dust accumulation, and shading effects. The system further generates outputs in the form of bounding boxes, segmentation maps, or heatmaps to indicate defect locations and severity. The invention supports real-time or near real-time processing and can be deployed on edge devices, cloud platforms, or integrated Internet of Things (IoT)-based monitoring systems for continuous surveillance and predictive maintenance. By reducing manual inspection efforts and improving detection accuracy, the proposed system enhances operational efficiency, reliability, and lifespan of solar panel installations.
Disclaimer: Curated by HT Syndication.
