GBH-YOLOv5: Ghost convolution with BottleneckCSP and tiny target prediction Head incorporating YOLOv5 for PV panel defect detection

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Contents

1. Project description
2. Required environment
3. Training
4. Testing
5. Acknowledgements

Citations

@article{Li2023,
title = {GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection},
shorttitle = {GBH-YOLOv5},
author = {Li, Longlong and Wang, Zhifeng and Zhang, Tingting},
year = {2023},
month = jan,
journal = {Electronics},
volume = {12},
number = {3},
pages = {1--15},
publisher = {Multidisciplinary Digital Publishing Institute},
issn = {2079-9292},
doi = {10.3390/electronics12030561},
copyright = {http://creativecommons.org/licenses/by/3.0/},
}

Project description

This project is based on the improved and optimized model of YOLOv5s, and its task is to detect defects on the surface of photovoltaic panels. In this study, the YOLOv5 model was improved to achieve 97.8% performance on PV Multi-Defect dataset. Research work based on this project has been submitted to 'Electronics', and the manuscript is titled "GBH-YOLOv5: Ghost convolution with BottleneckCSP and tiny target prediction Head incorporating YOLOv5 for PV paneldefect detection"

Required environment

Pytorch == 1.8.1
python == 3.8
Cuda == 11.1

Install the project environment

pip install -r requirements.txt

Check whether the dependent environment is normal.

python detect.py --source ./data/images/ --weights weights/yolov5s.pt

Training

Data preparation

Download PV Multi-Defect images (train, val) and labels. Link: https://github.com/CCNUZFW/PV-Multi-Defect.
Download weights. Link: https://pan.baidu.com/s/1ApCScpr1CZ_AeVJH7crgCw Extraction code: lmn8

Start training

python train.py

Testing

The optimal weight obtained from the training was used for the test.

python detect.py --source ./testfiles/img1.jpg --weights runs/train/exp/weights/best.pt

Acknowledgements

https://github.com/ultralytics/yolov5
https://github.com/robintzeng/Pytorch-CSPNet

If you have any question, please feel free to contact us through zfwang@ccnu.edu.cn.