Research on intelligent identification of typical construction defects of cable joints based on YOLO

Ling Wang; Jie Zhang

doi:10.1117/12.3048389

3 October 2024 Research on intelligent identification of typical construction defects of cable joints based on YOLO

Ling Wang, Jie Zhang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327231 (2024) https://doi.org/10.1117/12.3048389
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

The intermediate joint of the three-core winding package is a critical component in distribution network cable lines, and its standardization and consistency directly impact the safety and reliability of cable operations. However, due to the complex structure, high construction difficulty, and inconsistent acceptance standards of the intermediate joint, its quality can vary significantly, leading to potential hazards such as tip discharge, partial discharge, and overheating. To address these challenges, this paper introduces a deep neural network-based identification technology for the intermediate joints in three-core winding packages, utilizing the YOLO algorithm. By intelligently analyzing and assessing key characteristics such as shape, size, and position, our method enables automated identification and evaluation, thereby providing a scientific foundation and technical support for improving the quality control and reliability of cable joint production. This innovative approach significantly enhances the accuracy and efficiency of defect detection, ultimately contributing to enhanced safety and operational reliability in the field.

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

Citation Download Citation

Ling Wang and Jie Zhang "Research on intelligent identification of typical construction defects of cable joints based on YOLO", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327231 (3 October 2024); https://doi.org/10.1117/12.3048389

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KEYWORDS

Data modeling

Defect detection

Detection and tracking algorithms

Image processing

Education and training

Object detection

Statistical modeling

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