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:2019,28(11):265-270
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基于Faster RCNN的红外热图像热斑缺陷检测研究
(中国计量大学 机电工程学院, 杭州 310018)
Hot Spot Defect Detection Based on Infrared Thermal Image and Faster RCNN
(College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China)
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投稿时间:2019-04-07    修订日期:2019-05-08
中文摘要: 光伏组件在日常运行中不可避免会产生各种缺陷,热斑缺陷就是其中一种.现有的研究主要针对光伏组件在生产工艺流程中出现的缺陷,对日常运行中光伏组件产生的缺陷检测算法研究很少并且存在泛化能力差、准确率不足等问题.本文在原始Faster RCNN的基础上,结合图像预处理、迁移学习、改进特征提取网络模型以及改进锚框选区方案,得到热斑缺陷检测模型.实验证明,使用本文模型在自制的测试集上平均检测准确率可达97.34%,相比原始Faster RCNN提高了4.51%.
中文关键词: 光伏组件  热斑缺陷  Faster RCNN
Abstract:Photovoltaic modules inevitably produce various defects in daily operation, and hot spot defects are one of them. The existing research mainly focuses on the defects of photovoltaic modules in the production process, and there is few research on the defect detection algorithms generated by PV modules in daily operation, and there are problems such as poor generalization ability and insufficient accuracy. Based on the original faster RCNN, this study combines image preprocessing, migration learning, improved feature extraction network model, and improved anchor frame selection scheme to obtain hot spot defect detection model. Experiments show that the average detection accuracy of the self-made test set using this model is 97.34%, which is 4.51% higher than the original faster RCNN.
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郭梦浩,徐红伟.基于Faster RCNN的红外热图像热斑缺陷检测研究.计算机系统应用,2019,28(11):265-270
GUO Meng-Hao,XU Hong-Wei.Hot Spot Defect Detection Based on Infrared Thermal Image and Faster RCNN.COMPUTER SYSTEMS APPLICATIONS,2019,28(11):265-270

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