CN109615627B - 一种输变电巡检图像质量评价方法及系统 - Google Patents
一种输变电巡检图像质量评价方法及系统 Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
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CN110210558B (zh) * | 2019-05-31 | 2021-10-26 | 北京市商汤科技开发有限公司 | 评估神经网络性能的方法及装置 |
CN111325711A (zh) * | 2020-01-16 | 2020-06-23 | 杭州德适生物科技有限公司 | 一种基于深度学习的染色体分裂相图像质量评价方法 |
CN111818363A (zh) * | 2020-07-10 | 2020-10-23 | 携程计算机技术(上海)有限公司 | 短视频提取方法、系统、设备及存储介质 |
CN113158777B (zh) * | 2021-03-08 | 2024-07-02 | 佳都科技集团股份有限公司 | 质量评分方法、质量评分模型的训练方法及相关装置 |
CN113408695B (zh) * | 2021-04-29 | 2024-05-31 | 开放智能机器(上海)有限公司 | 一种离线量化工具的精度调优方法 |
CN113496485B (zh) * | 2021-06-24 | 2022-12-02 | 北京市遥感信息研究所 | 卫星遥感图像质量评价方法及装置 |
CN115766467A (zh) * | 2022-11-17 | 2023-03-07 | 国网甘肃省电力公司经济技术研究院 | 一种基于卷积神经网络的电力通信劣质环网拓扑识别方法 |
CN117975190B (zh) * | 2023-12-29 | 2024-11-05 | 中国科学院自动化研究所 | 基于视觉预训练模型的模仿学习混合样本处理方法及装置 |
CN118780693B (zh) * | 2024-09-12 | 2025-01-28 | 松立控股集团股份有限公司 | 一种巡检员工作评价方法、系统、电子设备及介质 |
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CN106503366A (zh) * | 2016-10-27 | 2017-03-15 | 石家庄铁道大学 | 抑制emd端点效应的方法 |
CN107743225A (zh) * | 2017-10-16 | 2018-02-27 | 杭州电子科技大学 | 一种利用多层深度表征进行无参考图像质量预测的方法 |
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CN103544705B (zh) * | 2013-10-25 | 2016-03-02 | 华南理工大学 | 一种基于深度卷积神经网络的图像质量测试方法 |
US9779492B1 (en) * | 2016-03-15 | 2017-10-03 | International Business Machines Corporation | Retinal image quality assessment, error identification and automatic quality correction |
US10002415B2 (en) * | 2016-04-12 | 2018-06-19 | Adobe Systems Incorporated | Utilizing deep learning for rating aesthetics of digital images |
CN107633513B (zh) * | 2017-09-18 | 2021-08-17 | 天津大学 | 基于深度学习的3d图像质量的度量方法 |
CN107610123A (zh) * | 2017-10-11 | 2018-01-19 | 中共中央办公厅电子科技学院 | 一种基于深度卷积神经网络的图像美学质量评价方法 |
CN108377387A (zh) * | 2018-03-22 | 2018-08-07 | 天津大学 | 基于3d卷积神经网络的虚拟现实视频质量评价方法 |
CN108596902B (zh) * | 2018-05-04 | 2020-09-08 | 北京大学 | 基于选通卷积神经网络的多任务全参考图像质量评价方法 |
CN108897797A (zh) * | 2018-06-12 | 2018-11-27 | 腾讯科技(深圳)有限公司 | 对话模型的更新训练方法、装置、存储介质及电子设备 |
CN108960087A (zh) * | 2018-06-20 | 2018-12-07 | 中国科学院重庆绿色智能技术研究院 | 一种基于多维度评估标准的人脸图像质量评估方法及系统 |
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CN106503366A (zh) * | 2016-10-27 | 2017-03-15 | 石家庄铁道大学 | 抑制emd端点效应的方法 |
CN107743225A (zh) * | 2017-10-16 | 2018-02-27 | 杭州电子科技大学 | 一种利用多层深度表征进行无参考图像质量预测的方法 |
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