基于迁移学习的光伏组件鸟粪覆盖检测

doi:10.19912/j.0254-0096.tynxb.2020-1398

太阳能学报 ›› 2022, Vol. 43 ›› Issue (2): 233-237.DOI: 10.19912/j.0254-0096.tynxb.2020-1398

基于迁移学习的光伏组件鸟粪覆盖检测

李琼¹, 吴文宝², 刘斌¹, 刘君¹

1.南昌航空大学信息工程学院,南昌 330063;
2.中国电建集团江西省电力建设有限公司,南昌 330001

收稿日期:2020-12-25 出版日期:2022-02-28 发布日期:2022-08-28
通讯作者: 李琼（1989—）,女,博士、高级工程师,主要从事光伏发电方面的研究。liqiong471255611@163.com
基金资助:
江西省科技厅重点研发项目（20192BBE50057）

BIRD DROPPINGS COVERAGE DETECTION OF PHOTOVOLTAIC MODULE BASED ON TRANSFER LEARNING

Li Qiong¹, Wu Wenbao², Liu Bin¹, Liu Jun¹

1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;
2. Jiangxi Electric Power Construction Co., Ltd. of China Power Construction Group, Nanchang 330001, China

Received:2020-12-25 Online:2022-02-28 Published:2022-08-28

摘要/Abstract

摘要： 该文基于无人机光伏组件可见光图像采集,提出一种基于迁移学习的光伏组件鸟粪检测方法。方法首先基于掩膜区域卷积神经网络（Mask-RCNN）对光伏组件边界进行框选,再利用迁移学习策略,构建光伏组件鸟粪缺陷检测模型,实现鸟粪智能检测。利用上述方法,实现光伏组件鸟粪覆盖检测准确率为96.75%。

关键词: 迁移学习, 光伏组件, 掩膜区域卷积神经网络, 缺陷检测

Abstract: In this paper, based on the visible light image acquisition of photovoltaic（PV） panel by using unmanned aerial vehicle, a bird droppings detection method of photovoltaic panel based on transfer learning is proposed. Firstly, the boundary of PV panel is selected based on Mask-RCNN, and then the bird droppings defect detection model of PV panel was constructed by using transfer learning strategy to realize intelligent detection of bird droppings. Using the above method, the detection accuracy of bird droppings coverage of PV panel is 96.75%.

Key words: transfer learning, photovoltaic modules, Mask-RCNN, defect detection

中图分类号:

TK51

李琼, 吴文宝, 刘斌, 刘君. 基于迁移学习的光伏组件鸟粪覆盖检测[J]. 太阳能学报, 2022, 43(2): 233-237.

Li Qiong, Wu Wenbao, Liu Bin, Liu Jun. BIRD DROPPINGS COVERAGE DETECTION OF PHOTOVOLTAIC MODULE BASED ON TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica, 2022, 43(2): 233-237.

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基于迁移学习的光伏组件鸟粪覆盖检测

BIRD DROPPINGS COVERAGE DETECTION OF PHOTOVOLTAIC MODULE BASED ON TRANSFER LEARNING

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