Photovoltaic panel dust camera

How to detect surface dust on solar photovoltaic panels?

At present, the main methods for detecting surface dust on solar photovoltaic panels include object detection, image segmentation and instance segmentation, super-resolution image generation, multispectral and thermal infrared imaging, and deep learning methods.

Does dust accumulate on PV panels?

In this paper, a novel image enhancement algorithm is proposed to evaluate the dust accumulation on PV panels. An atmospheric scattering model was used to analyze the difference in the image characteristics of clean and dusty PV panels.

Are surface dust detection algorithms effective in solar photovoltaic panels?

Specifically, extensive and in-depth validation experiments have been conducted on the surface dust detection dataset of solar photovoltaic panels. The experimental results clearly demonstrate the effectiveness and excellent performance of the improved algorithm in this field.

How is solar photovoltaic panel dust detection data processed?

In terms of data processing, we adopted the solar photovoltaic panel dust detection dataset and divided the data into training, validation, and testing sets in a strict 7:2:1 ratio to ensure that the quality and quantity of training, validation, and testing data are fully guaranteed.

Can imaging process technology detect dust on PV panels?

Researchers in [18, 19] investigated the feasibility of imaging process technology to detect dust on PV panels. An approach based on optical imaging and the routine measurement of aerosols was also explored in to obtain PV panels’ dust.

Do neural networks improve dust detection algorithms in solar photovoltaic panels?

In order to compare the performance of improved algorithms in different neural network architectures and highlight the comprehensiveness of the comparative experiment, we conducted experiments on the dust detection dataset of solar photovoltaic panels on three different neural networks: ResNet-18, VGG-16, and MobileNetV2.