insulator-defect-detection

Insulator-Defect Detection Algorithm Based on Improved YOLOv7

https://github.com/dataset-ninja/insulator-defect-detection

Science Score: 44.0%

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Repository

Insulator-Defect Detection Algorithm Based on Improved YOLOv7

Basic Info

Host: GitHub
Owner: dataset-ninja
License: other
Language: Python
Default Branch: main
Size: 24.4 MB

Statistics

Stars: 1
Watchers: 1
Forks: 2
Open Issues: 0
Releases: 0

Created over 2 years ago · Last pushed 7 months ago

Metadata Files

Readme License Citation

README.md

Insulator-Defect Detection

Insulator-Defect Detection is a dataset for object detection task.

Owner

Name: dataset-ninja
Login: dataset-ninja
Kind: organization

Repositories: 1
Profile: https://github.com/dataset-ninja

Citation (CITATION.md)

If you make use of the Insulator-Defect Detection data, please cite the following reference:

```bibtex
@Article{s22228801,
    AUTHOR = {Zheng, Jianfeng and Wu, Hang and Zhang, Han and Wang, Zhaoqi and Xu, Weiyue},
    TITLE = {Insulator-Defect Detection Algorithm Based on Improved YOLOv7},
    JOURNAL = {Sensors},
    VOLUME = {22},
    YEAR = {2022},
    NUMBER = {22},
    ARTICLE-NUMBER = {8801},
    URL = {https://www.mdpi.com/1424-8220/22/22/8801},
    PubMedID = {36433397},
    ISSN = {1424-8220},
    ABSTRACT = {Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.},
    DOI = {10.3390/s22228801}
}
```

[Source](https://www.mdpi.com/1424-8220/22/22/8801)

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