Deep learning transmission line defect recognition based on improved Yolov5

Jiang Peng; Bo Peng; Xiulong Wang; Liwei Zhou; Guoliang Zhang; Qingyu Kong

doi:10.1117/12.3026430

27 March 2024 Deep learning transmission line defect recognition based on improved Yolov5

Jiang Peng, Bo Peng, Xiulong Wang, Liwei Zhou, Guoliang Zhang, Qingyu Kong

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052S (2024) https://doi.org/10.1117/12.3026430
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Aiming at the problem of slow and inaccurate positioning of defects based on deep learning transmission line defect recognition model, an intelligent detection method based on improved Yolov5 transmission line image recognition is proposed. Firstly, according to the relevant characteristics of the transmission line data set, relevant features are extracted to obtain sufficient sample data. Secondly, the lightweight model of convolutional neural network is constructed, and the model training is completed by using the obtained transmission line sample data. Finally, the location and classification of transmission line defects in complex background are realized through multiple transmission line training sets and test sets. The results show that the proposed algorithm has the highest detection accuracy, and the average detection accuracy can reach 98.7 %. It is a simple, effective and practical transmission line defect recognition method.

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

Citation Download Citation

Jiang Peng, Bo Peng, Xiulong Wang, Liwei Zhou, Guoliang Zhang, and Qingyu Kong "Deep learning transmission line defect recognition based on improved Yolov5", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052S (27 March 2024); https://doi.org/10.1117/12.3026430

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KEYWORDS

Deep learning

Data modeling

Detection and tracking algorithms

Target detection

Transformers

Education and training

Image analysis

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