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5 / 2023-08-18 15:05:11
Insulator Defect Detection based on Faster R-CNN and YOLOv3 Algorithm
Insulator; Faster R-CNN; YOLOv3; EfficientNet-B3; CBAM
6.AI-driven technology > (1)、Intelligent Anomaly Detection, Fault Diagnosis and Prognostics
Final Paper
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
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.

 
Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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