A Deep Learning Based Detection Method for Insulator Defects in High Voltage Transmission Lines

The high-voltage transmission system is a key component of the power network, and the reliability of its insulators directly affects the safe operation of the system. Traditional insulator defect detection methods are reliant on manual inspection, which requires significant human resources and is prone to substantial subjectivity. To address this issue, this paper proposes an insulator defect recognition method based on the improved YOLOv5 algorithm. This method first collects images of insulator defects and then utilizes the YOLOv5 model for recognition training. To enhance multi-scale feature fusion capability, a bidirectional feature pyramid network (BiFPN) is introduced. During the training process, the SiUL function is used, and the SE attention mechanism has been integrated into the detection backbone network, which enhances the model's detection accuracy. Experimental results show that the model achieves a detection precision of 90.27%, a recall of 89.14%, and a mAP of 91.34% on the test set. To further enhance the model's practicality, a PyQt5-based user interface (GUI) for the inspection system is designed, enabling interactive functions such as image uploading, defect detection, and result display. In summary, the research presented in this paper provides efficient and accurate technical support for intelligent power inspection, offering a wide range of application prospects.

@article{Tingyu2024,
  title     = {A Deep Learning Based Detection Method for Insulator Defects in High Voltage Transmission Lines},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wang Tingyu and Sun Xia and Liu Jiaxing and Zhang Yue},
  doi       = {10.14569/IJACSA.2024.0151040},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151040}
}