Research on transmission line defect identification method based on computer vision

Li, Mengxuan; Ma, Xiao; Yang, Zhiyuan; Zhao, Bi; Han, Jingshan; Liu, Bin

doi:10.1145/3512576.3512584

Cited by 1 publication

(1 citation statement)

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“…In terms of the related research, the reference [1] proposes an end-to-end deep learning algorithm with high recognition accuracy based on R-CNN to establish a detection model with the transfer learning and the fine tuning, aiming at the problems such as the few defect categories and no unified detection parameters in the detection of the transmission lines. The reference [2] proposes a transmission line defect detection method based on the improved YOLOv3 algorithm, and adds spatial pyramid pooling module under the original basic framework of the YOLOv3, so as to adapt to the image detection with the different input sizes and complex changes on the inspection site.…”

Section: Introductionmentioning

confidence: 99%

Transmission equipment defect identification algorithm based on the VIT large model architecture

Li,

Wang,

Zhou

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Aiming at the transmission defect business scenario with many components, various defect forms and uneven size distribution, this paper proposes a transmission defect identification algorithm based on the VIT pre-trained visual large model architecture and the ViTDet object detection training algorithm. Specifically, it uses the ViT-Large model as the backbone network and Cascade-rcnn as the framework of the ViTDet algorithm. Meanwhile, in order to solve the problem of small-size defect identification in transmission scenes with large field of view, the image clipping training strategy is integrated. Cut each image equally into four parts with an overlap rate of 20% during training. In the case of the similar false detection, the recognition rate of the large model has an improvement of about 5% compared to the traditional CNN model.

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Section: Introductionmentioning

confidence: 99%