A deep learning-based method detects dust from solar PV panels through Unmanned Aerial Vehicles

Abstract

Abstract As the number of solar photovoltaic (PV) panels increases, dust detection on the panels becomes particularly important. In this paper, we propose a deep learning-based method that detects dust from solar PV panels through Unmanned Aerial Vehicles. The model utilizes the improved YOLOv5 method to detect PV panel dust on aerial images. The model is a lightweight model that requires fewer computing resources and time and can work in real time on a regular CPU computer. Moreover, in this paper, a prediction head is added to YOLOv5 to cope with significant changes in target scales due to unmanned aerial vehicles capturing images at different altitudes. And the model introduces new tricks to help detect dust targets in images with large coverage areas. After experimental validation, the proposed method outperforms the state-of-the-art in terms of detection accuracy, detection speed, F1 score, etc., and is more suitable for the inspection of dust on PV panels of Unmanned Aerial Vehicles.