Work

Solar Panel Defect Detection using FOMO on Edge Impulse

Year: 2025

Type: article

Abstract: A machine learning model is developed for detecting defects in solar panels, focusing on hotspot detection. The project was created on Edge Impulse with images of solar panels in normal and defective states. The trained model with appropriate training accuracy was then deployed on a smartphone for real-time defect detection, utilizing a camera to capture and process image frames, overlaying results on a smartphone screen for immediate inspection. The evaluation revealed an F1 score of 80.5% for hotspot detection with an accuracy of over 95% for the model-detecting hotspots. This demonstrates the model’s practical application and effectiveness for solar panel hotspot detection.

Source: Environment-Behaviour Proceedings Journal

DOI: 10.21834/e-bpj.v10isi27.6822

Authors: Muhammad Nabil Aiman Baderul Hisham, Roslan Seman, Zuraidi Saad, Zainal Hisham Che Soh

Institution: Universiti Teknologi MARA

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond

Funder: Universiti Teknologi MARA