Dust Detection Techniques for Photovoltaic Panels from a Machine Vision Perspective: A Review
Fuhao Sun, Cheng Yang, Haoyang Cui, Zhipeng Lv, Jie Shao, Bochao Zhao, Ke He
Abstract
This paper provides an extensive review of dust detection techniques for photovoltaic panels. The review is conducted from two main perspectives. Firstly, the paper examines the current state of research into image processing methods for detecting dust on photovoltaic panels, which includes an analysis of the various techniques and algorithms that have been developed to date. Secondly, the paper reviews deep learning-based techniques for dust detection on photovoltaic panels which includes an examination of how machine learning algorithms can be used to improve the accuracy and efficiency of dust detection. This paper highlights some of the key challenges and future research directions in the field of photovoltaic panel dust detection technology, which include improving the accuracy and reliability of dust detection methods, as well as developing new techniques that can better cope with changing environmental conditions. Overall, this paper provides a valuable overview of current research into dust detection techniques for photovoltaic panels and points towards some exciting future developments in this field.