Photovoltaic panel detection dataset

How accurate is the solar panel defect detection algorithm?

The results of comparative experiments on the solar panel defect detection data set show that after the improvement of the algorithm, the overall precision is increased by 1.5%, the recall rate is increased by 2.4%, and the mAP is up to 95.5%, which is 2.5% higher than that before the improvement.

Which dataset is used for PV panel segmentation?

The utilized dataset is from the multi-resolution dataset for PV panel segmentation published by Jiang et al. . This dataset contains 3716 samples annotated in Jiangsu Province, China, including different types of PVs such as centralized PVs, distributed ground-mounted PVs, and fine-grained rooftop PVs. ... ...

How to detect a defect in solar panels?

In order to avoid such accidents, it is a top priority to carry out relevant quality inspection before the solar panels leave the factory. For the defect detection of solar panels, the main traditional methods are divided into artificial physical method and machine vision method.

What is automatic defect detection & classification in solar cells?

Automatic defect detection and classification in solar cells is the subject of many publications since EL imaging of silicon solar cells was first introduced by Fuyuki et al. for detection of deteriorated areas in solar cells in 2005.

Do solar panels have object detection models?

Reports of solar panel installations have been supplemented with object detection models developed and used on openly available aerial imagery, a type of imagery collected by aircraft or drones and limited by cost, extent, and geographic location.

How much data does a grid-tie photovoltaic plant collect?

This dataset contains 16 days of data of a grid-tie photovoltaic plant's operation with both faulty and normal operation. The dataset is divided into 2 '.mat' files (which can be loaded with MATLAB). The photovoltaic plant used to collect this data has 2 strings with 8x C6SU-330P PV Modules each.