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Insulator Defect Detection based on Faster R-CNN and YOLOv3 Algorithm
编号：111 访问权限：仅限参会人 更新：2023-11-20 13:53:17 浏览：191次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

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摘要
In order to solve the problem that the existing insulator defect detection models are not effective enough for the tasks such as complex background and low signal-to-noise ratio of the target objects, this paper proposes an insulator defect detection method based on Faster R-CNN and YOLOv3 target detection algorithm. First, operations such as random angle rotation, flipping, adjusting contrast, and adding noise are used to preprocess the data images. Secondly, the Faster R-CNN algorithm is used as the basis to realize the accurate localization of insulators. Finally, EfficientNet-B3 is used as the backbone network of YOLOv3, and the dual-attention mechanism CBAM is embedded to realize the insulator defect recognition. The results show that compared with the existing models, the insulator defect detection method proposed in this paper exhibits more accurate insulator localization and defect recognition performance.
 
关键词
Insulator; Faster R-CNN; YOLOv3; EfficientNet-B3; CBAM
报告人
Junchen Lu
School of Big Health and Intelligent Engineering, Chengdu Medical College

稿件作者
Ping Hu School of Big Health and Intelligent Engineering, Chengdu Medical College
Junchen Lu School of Big Health and Intelligent Engineering, Chengdu Medical College
Yuan Cui School of Big Health and Intelligent Engineering, Chengdu Medical College
Bo Hu School of Big Health and Intelligent Engineering, Chengdu Medical College
Fan Liang Tangshan Research Institute,Southwest Jiaotong University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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