abylikhsanov / Insulator-Defect-Detection

Insulator-Defect-Detection

Official PyTorch Implementation of Catenary Insulator Defects Detection: A Dataset and an Unsupervised Baseline, Accepted by IEEE TIM.

Datasets

Catenary Insulator Defect dataset (CID): Download from [BaiDu] [Google Drive]

Statistical Overview of CID

		Train	Test_Real	Test_Sim	Test_All
Defect-free Image		3900	0	0	33
Defect Image	breakage	0	26	34	60
	contamination	0	5	55	60
	crack	0	2	58	60
	dirt	0	54	6	60
	missing	0	0	60	60
	shelter	0	40	20	60
Total		3900	160	233	393

Some Example of CID

The dataset should be placed in the 'data' folder. The training dataset should only contain defect-free samples, which should be named 'good'. The test dataset should include one category named 'good' for defect-free samples, and any other subcategories of defect samples. It should be made as follows:

data
|---- insulator
|-----|------ ground_truth
|-----|------|------ teat_all
|-----|------|------ test_real
|-----|------|------ test_sim
|-----|------ test_all
|-----|------|------ good
|-----|------|------ ...
|-----|------|------ ...
|-----|------ test_real
|-----|------|------ good
|-----|------|------ ...
|-----|------|------ ...
|-----|------ test_sim
|-----|------|------ good
|-----|------|------ ...
|-----|------|------ ...
|-----|------ train
|-----|------|------ good

Environment

This repository is implemented and tested on Python 3.9 and PyTorch 1.10. To install requirements:

pip install-r requirements.txt

Usage

To train theMAE model, run:

python train.py --batch_size 32 --epoch 2000 --model mae_vit_base_patch16 --output_dir ./checkpoints/insulator --log_dir ./checkpoints/insulator --data_path ./data/insulator --input_size 224 --blr 5e-3 --device_select 0

To evaluate and test the model, run:

for w/o pre
python inference.py --input_size=224 --mask_size=16 --test_fold="test_all" --save_dir ./results
for w/ pre
python inference_pre.py --input_size=224 --mask_size=16 --test_fold="test_real" --save_dir ./results_pre
for upper bound
python inference_ub.py --input_size=224 --mask_size=16 --test_fold="test_sim" --save_dir ./results_ub

Before running, you can download the model checkpoints directly from [BaiDu] The checkpoints should be placed in the 'checkpoints' folder. It should be made as follows:

checkpoints
|---- insulator
|-----|------ 224-10.pth
|-----|------ 224-20.pth
|-----|------ 224.txt
|-----|------ ...
|-----|------ ...
|-----|------ ...

Result

Running the code as explained in this file should achieve the following results for CID:

Defect Detection (Image AUROC, P, R, F1) and Defect Localization (Pixel AUROC, P, R, F1)

	Method	AUROC	P	R	F1
Image-level Detection	w/o pre	99.21/99.48/99.39	98.44/98.72/99.17	99.21/99.14/99.21	0.99/0.99/0.99
	w/ pre	99.24/99.60/99.47	98.44/99.14/99.44	99.21/99.14/99.17	0.99/0.99/0.99
	Upper Bound	100.0/100.0/100.0	100.0/100.0/100.0	100.0/100.0/100.0	1.00/1.00/1.00
Pixel-level Localization	w/o pre	95.59/98.18/97.13	58.05/66.59/63.58	72.15/77.31/75.51	0.63/0.70/0.68
	w/ pre	95.70/98.29/97.37	58.06/66.53/63.52	71.67/77.15/75.26	0.63/0.70/0.68
	Upper Bound	96.94/99.49/98.59	59.38/68.73/65.42	75.20/79.34/77.90	0.65/0.73/0.70

Visualization examples

Acknowledgement

We use some codes from repositories including MAE

License

This project is under the MIT license. See LICENSE for details.