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

Zhenyu Li; Wanguo Wang; Liwei Zhou; Guangxiu Liu; Qi Wang; Tong Wang

doi:10.1117/12.3026597

27 March 2024 Transmission equipment defect identification algorithm based on the VIT large model architecture

Zhenyu Li, Wanguo Wang, Liwei Zhou, Guangxiu Liu, Qi Wang, Tong Wang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310532 (2024) https://doi.org/10.1117/12.3026597
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenyu Li, Wanguo Wang, Liwei Zhou, Guangxiu Liu, Qi Wang, and Tong Wang "Transmission equipment defect identification algorithm based on the VIT large model architecture", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310532 (27 March 2024); https://doi.org/10.1117/12.3026597

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KEYWORDS

Education and training

Object detection

Visual process modeling

Detection and tracking algorithms

Transformers

Data modeling

Inspection

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