LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection

Dataset

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

Introduction

This is our PyTorch implementation of the paper "LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection" published in IEEE Transactions on Instrumentation and Measurement.

Quick Start Examples

Install

First, clone the project and configure the environment. Python>=3.7.0, PyTorch>=1.7.

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

Train

python train.py --cfg models/LiteYOLO-ID.yaml --data data/mydata.yaml

Test

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

EGC Schematic Diagram

The lightweight convolutional module EGC incorporates the design philosophies of GhostNet and C2f modules, significantly enhancing the capture of key information in detection targets through the ECA attention mechanism. The structural diagram of the EGC module is shown below.

Experimental flow chart

Actual detection output on Jetson TX2 NX

The hardware and software configuration of the Jetson TX2 NX includes an NVIDIA Pascal GPU, with PyTorch version 1.8.0 and CUDA version 10.2.

Detection result

Citation

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

@ARTICLE{10569022,
  author = {Li, Dahua and Lu, Yang and Gao, Qiang and Li, Xuan and Yu, Xiao and Song, Yu},
  title = {LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection},
  journal = {IEEE Transactions on Instrumentation and Measurement},
  year = {2024},
  volume = {73},
  pages = {1-12},
  doi = {10.1109/TIM.2024.3418082},
  keywords = {Insulators, Accuracy, Computational modeling, Defect detection, YOLO, Feature extraction, Neck, Deep learning, insulator defect detection, lightweight, quantification, deployment}
}

Author's Contact

Email：yj20220275@stud.tjut.edu.cn