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作 者:田苏慧敏 吴登峰 李银斌 胡艳茹 赵飞燕 TIAN Suhuimin;WU Dengfeng;LI Yinbin;HU Yanru;ZHAO Feiyan(School of Physics and Electronic Information Engineering,Ningxia Normal University,Guyuan 756099,China;State Grid Guyuan Electric Power Company,Guyuan 756099,China)
机构地区:[1]宁夏师范大学物理与电子信息工程学院,宁夏固原756099 [2]国网固原供电公司,宁夏固原756099
出 处:《现代信息科技》2025年第1期54-59,共6页Modern Information Technology
基 金:宁夏自然科学基金项目(2023AAC03353)。
摘 要:变压器作为电力系统的关键设备,其安全可靠运行显得尤为重要,变压器套管的发热缺陷是影响其运行可靠性的重要因素之一。针对变压器套管红外图像发热缺陷识别问题,引入YOLOv8目标检测算法。首先,分析了变压器套管发热缺陷的产生原因、传统变压器套管发热缺陷红外识别方法及其存在的不足。其次,对比分析了中值滤波、引导滤波两种不同去噪方法。然后,设计增广、增强+增广两种不同方法构建数据集,基于YOLOv8算法分别进行训练。最后,在测试集上进行对比实验,结果表明,增强+增广处理后训练所得模型的识别精确率提高了3%、召回率提高了2.3%、平均精度均值提高了0.8%。As the critical equipment of the power system,the safe and reliable operation of transformers is of paramount importance.The thermal defect in transformer bushing is one of the significant factors affecting the operational reliability.To address the issue of thermal defect identification in infrared images of transformer bushings,the YOLOv8 object detection algorithm is introduced.Firstly,the causes of thermal defects in transformer bushings are analyzed,along with thermal defect infrared identification methods of traditional transformer bushings and the existing limitations.Subsequently,it compares and analyzes the two different denoising methods of medianfiltering and guidedfiltering.Next,it designs two different methods of augmentation and enhancement+augmentation to construct the dataset,and conducts the training based on YOLOv8 algorithm separately.Finally,comparative experiments are conducted on the test set.The results demonstrate that the model after training with the process of enhancement+augmentation achieves a 3%improvement in recognition precision,a 2.3%increase in recall,and a 0.8%increase in average value of mean accuracy.
关 键 词:YOLOv8 变压器套管 红外图像 发热缺陷 图像去噪 图像增强 图像增广 目标识别
分 类 号:TM41[电气工程—电器] TP183[自动化与计算机技术—控制理论与控制工程]
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