I will first provide links to Baidu.com discs for some of the datasets. More specific datasets involve confidential information from Tianjin Grid, and I need to discuss with them whether they can be made public. However, the datasets I provided are sufficient for code debugging and model training. The links to the datasets are below:

Link: https://pan.baidu.com/s/1inULMZcnibOsfjXvJQiFjQ

Extraction code: 8kdy

Google Drive download link： https://drive.google.com/file/d/1QOFg5iCop9Jb9uAjiNelYvELOmUWGMs-/view?usp=drive_link

This is our PyTorch implementation of the paper "IDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection Algorithm" published in 2024 IEEE International Conference on Mechatronics and Automation (ICMA).

git clone https://github.com/LuYang-2023/ICMA2024.git  # clone
cd ICMA2024
pip install -r requirements.txt  # install

python train.py --cfg models/IDD-yolov5.yaml --data data/insulator_detection.yaml

python val.py --data data/mydata.yaml --weights best.pt --task test

If you use this code or article in your research, please cite it using the following BibTeX entry:

@INPROCEEDINGS{10632897,
  author={Lu, Yang and Li, Dahua and Gao, Qiang and Yu, Xiao and Li, Xuan and Bai, Zhongli},
  booktitle={2024 IEEE International Conference on Mechatronics and Automation (ICMA)}, 
  title={IDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection Algorithm}, 
  year={2024},
  volume={},
  number={},
  pages={491-495},
  keywords={YOLO;Adaptation models;Accuracy;Power transmission lines;Insulators;Real-time systems;Neck;Defect detection;Insulator;Lightweight;Deep learning;YOLOv5},
  doi={10.1109/ICMA61710.2024.10632897}}

Email：[email protected]