A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects
Abstract
:1. Introduction
2. Related Works
2.1. Unsupervised Detection Method
2.2. Denoising Diffusion Probabilistic Models
3. Proposed Methods
- (1)
- Surface Feature Extraction of Flawless Fabrics
- (2)
- Defect Detection with SN-DDPM
3.1. Surface Feature Extraction of Flawless Fabrics
3.1.1. Forward Diffusion
3.1.2. Simplex Noise
3.1.3. Reverse Diffusion
3.1.4. Denoising Unet
3.1.5. Timestep Adaptive Module
3.2. Defect Detection with SN-DDPM
Algorithm 1: Defect Detection with SN-DDPM |
Input: RGB image Output: Defect detection result 1: Step 1: Obtaining the optimal timestep and reconstructing the defect image . 2: Step 2: Processing the images as follows: 3: Converting the RGB image to grayscale: 4: Gaussian filter: 5: Step 3: Absolute difference: 6: 7: Step 4: Performing FTSD: 8: Applying the Gaussian filter to smooth the residual image 9: Converting the smoothed image to LAB color space 10: Calculating the average image feature vector 11: Calculating the pixel vector value 12: Calculating the saliency image from normalized Euclidean distance 13: Step 5: Binarization: 14: Calculating the threshold value: 15: Binarizing the saliency image: 16: Step 6: Closed operation: 17: |
4. Experimental Setup
4.1. Datasets
4.2. Training Process
4.3. Evaluation Method
4.3.1. Evaluation Indicator of Image Reconstruction Results
4.3.2. Evaluation Indicator Defect Detection Results
5. Experimental Results and Discussion
5.1. Fabric Images Reconstruction Experiments
5.2. Defect Detection Experiments
5.3. Ablation Study
5.4. Model Failure Experiment
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Index | Method | SL1 | SL2 | SL5 | SL8 | SL9 | SL10 | SL11 | SL13 | Average Value |
---|---|---|---|---|---|---|---|---|---|---|
SSIM | DCAE | 0.9584 | 0.8035 | 0.8264 | 0.9341 | 0.6942 | 0.8907 | 0.7530 | 0.8886 | 0.8436 |
DCGAN | 0.5477 | 0.1682 | 0.5392 | 0.0986 | 0.7462 | 0.3840 | 0.3568 | 0.0460 | 0.3608 | |
Recycle-GAN | 0.0151 | 0.2721 | 0.1397 | 0.3643 | 0.0787 | 0.3472 | 0.1495 | 0.0330 | 0.1750 | |
MSCDAE | 0.4084 | 0.4238 | 0.1586 | 0.3286 | 0.7645 | 0.3988 | 0.4672 | 0.4385 | 0.4236 | |
UDCAE | 0.9558 | 0.7956 | 0.8234 | 0.8267 | 0.8564 | 0.8462 | 0.7869 | 0.7093 | 0.8250 | |
VAE-L2SSIM | 0.5703 | 0.1699 | 0.3295 | 0.3885 | 0.4695 | 0.4629 | 0.5428 | 0.3921 | 0.4157 | |
AFFGAN | 0.9748 | 0.8542 | 0.8594 | 0.8693 | 0.9135 | 0.9491 | 0.9446 | 0.9136 | 0.9098 | |
SN-DDPM | 0.9646 | 0.9029 | 0.8697 | 0.8938 | 0.9396 | 0.9077 | 0.9481 | 0.9591 | 0.9232 | |
PSNR (dB) | DCAE | 26.2641 | 26.7438 | 27.4138 | 27.1108 | 24.9037 | 28.3805 | 27.9167 | 28.6731 | 27.1758 |
DCGAN | 14.8116 | 12.8657 | 14.5869 | 8.1876 | 14.6379 | 13.8489 | 13.2846 | 12.2010 | 13.0530 | |
Recycle-GAN | 11.7674 | 17.5644 | 11.7564 | 18.3723 | 18.9168 | 19.0082 | 14.6736 | 11.0379 | 15.3871 | |
MSCDAE | 19.9604 | 21.7564 | 12.4692 | 14.8990 | 24.9513 | 23.1437 | 21.2004 | 25.1333 | 20.4392 | |
UDCAE | 25.3496 | 25.6432 | 25.3891 | 22.8675 | 26.3204 | 25.6267 | 21.9769 | 25.0596 | 24.7791 | |
VAE-L2SSIM | 20.8485 | 10.0348 | 12.8676 | 24.6738 | 18.7857 | 22.9767 | 20.6472 | 26.1235 | 19.6197 | |
AFFGAN | 28.1567 | 28.9947 | 27.0947 | 27.8877 | 28.9254 | 29.3189 | 30.0192 | 27.7191 | 28.5146 | |
SN-DDPM | 28.1464 | 29.8400 | 25.4589 | 27.8956 | 29.1771 | 30.2919 | 28.3950 | 30.1329 | 28.6672 |
Metric (%) | Method | SL1 | SL2 | SL5 | SL8 | SL9 | SL10 | SL11 | SL13 | Average Value |
---|---|---|---|---|---|---|---|---|---|---|
P | DCAE | 37.92 | 37.73 | 48.87 | 63.49 | 16.29 | 46.59 | 55.83 | 47.52 | 44.28 |
DCGAN | 22.29 | 38.13 | 66.45 | 31.91 | 16.23 | 8.76 | 0.00 | 0.00 | 22.97 | |
Recycle-GAN | 36.24 | 25.39 | 20.33 | 42.77 | 31.25 | 23.85 | 35.78 | 44.15 | 32.47 | |
MSCDAE | 51.39 | 36.17 | 49.68 | 56.78 | 44.68 | 43.66 | 54.09 | 49.68 | 48.27 | |
UDCAE | 54.94 | 55.55 | 87.75 | 15.53 | 59.14 | 51.14 | 15.69 | 87.75 | 53.44 | |
VAE-L2SSIM | 0.00 | 42.69 | 25.00 | 70.13 | 14.28 | 24.48 | 2.28 | 24.06 | 25.37 | |
AFFGAN | 62.01 | 17.02 | 21.84 | 63.26 | 35.85 | 47.69 | 34.67 | 29.86 | 39.02 | |
SN-DDPM | 61.10 | 58.97 | 33.48 | 57.45 | 60.47 | 51.04 | 61.44 | 46.10 | 53.76 | |
R | DCAE | 72.92 | 65.04 | 51.57 | 81.08 | 13.51 | 62.74 | 60.03 | 65.80 | 59.09 |
DCGAN | 20.08 | 35.93 | 6.70 | 17.73 | 10.00 | 1.00 | 0.00 | 99.44 | 23.86 | |
Recycle-GAN | 79.56 | 60.22 | 56.68 | 73.87 | 67.80 | 83.46 | 74.28 | 75.27 | 71.39 | |
MSCDAE | 74.44 | 74.15 | 71.15 | 86.55 | 26.03 | 71.23 | 76.19 | 71.15 | 68.86 | |
UDCAE | 82.11 | 61.61 | 35.66 | 8.08 | 78.45 | 44.20 | 15.12 | 35.66 | 45.11 | |
VAE-L2SSIM | 0.00 | 14.14 | 0.99 | 59.60 | 22.50 | 2.81 | 11.66 | 34.10 | 18.22 | |
AFFGAN | 75.89 | 57.42 | 69.09 | 79.30 | 80.57 | 64.41 | 38.12 | 44.79 | 63.70 | |
SN-DDPM | 83.07 | 70.61 | 87.01 | 84.20 | 64.11 | 83.65 | 80.89 | 76.92 | 78.81 | |
Acc | DCAE | 98.36 | 97.85 | 96.97 | 99.23 | 97.99 | 98.59 | 99.26 | 99.37 | 98.45 |
DCGAN | 97.63 | 98.93 | 97.03 | 99.17 | 97.84 | 98.84 | 99.15 | 0.00 | 86.07 | |
Recycle-GAN | 99.09 | 97.69 | 97.47 | 99.05 | 98.26 | 99.01 | 99.25 | 99.10 | 98.62 | |
MSCDAE | 98.78 | 97.52 | 94.92 | 99.24 | 98.23 | 98.53 | 99.23 | 94.92 | 97.67 | |
UDCAE | 98.94 | 98.74 | 97.84 | 99.00 | 98.67 | 98.75 | 99.21 | 97.84 | 98.62 | |
VAE-L2SSIM | 98.68 | 98.72 | 96.90 | 99.36 | 98.27 | 98.85 | 99.53 | 99.53 | 98.73 | |
AFFGAN | 99.16 | 97.34 | 97.97 | 99.23 | 99.82 | 98.66 | 99.17 | 99.55 | 98.86 | |
SN-DDPM | 99.36 | 97.87 | 97.61 | 99.40 | 99.42 | 99.42 | 99.62 | 98.90 | 98.95 | |
F1 | DCAE | 46.55 | 46.41 | 48.02 | 67.26 | 14.74 | 50.17 | 52.60 | 45.36 | 46.39 |
DCGAN | 16.32 | 36.24 | 10.48 | 21.72 | 5.36 | 1.75 | 0.00 | 0.00 | 11.48 | |
Recycle-GAN | 46.05 | 24.97 | 27.65 | 0.00 | 37.88 | 33.31 | 44.08 | 52.51 | 33.31 | |
MSCDAE | 58.40 | 47.29 | 57.64 | 66.59 | 25.68 | 51.90 | 59.66 | 57.64 | 53.10 | |
UDCAE | 63.17 | 53.36 | 46.99 | 8.63 | 60.60 | 39.22 | 13.14 | 46.99 | 41.51 | |
VAE-L2SSIM | 0.00 | 19.34 | 1.90 | 63.68 | 15.04 | 4.86 | 22.42 | 22.42 | 18.71 | |
AFFGAN | 65.15 | 16.41 | 31.53 | 66.57 | 49.62 | 52.57 | 32.35 | 29.93 | 43.02 | |
SN-DDPM | 65.62 | 55.61 | 44.77 | 64.67 | 61.44 | 57.11 | 64.76 | 54.20 | 58.52 | |
IoU | DCAE | 31.45 | 31.85 | 32.98 | 52.24 | 23.87 | 34.98 | 38.03 | 30.42 | 34.48 |
DCGAN | 10.11 | 28.70 | 6.69 | 15.12 | 2.96 | 0.99 | 0.00 | 0.00 | 8.07 | |
Recycle-GAN | 33.22 | 16.81 | 16.54 | 38.22 | 23.83 | 21.54 | 30.60 | 39.17 | 27.49 | |
MSCDAE | 42.80 | 31.25 | 44.59 | 50.91 | 17.45 | 36.50 | 44.07 | 44.59 | 39.02 | |
UDCAE | 47.31 | 39.43 | 32.49 | 6.39 | 44.06 | 26.37 | 8.62 | 32.49 | 29.65 | |
VAE-L2SSIM | 0.00 | 13.40 | 0.99 | 48.65 | 9.44 | 2.75 | 12.76 | 12.76 | 12.59 | |
AFFGAN | 50.09 | 9.33 | 19.18 | 51.32 | 33.00 | 37.14 | 25.48 | 20.84 | 30.80 | |
SN-DDPM | 53.25 | 47.25 | 31.52 | 50.94 | 48.92 | 46.80 | 54.09 | 40.26 | 46.63 |
Metric (%) | α | SL1 | SL2 | SL8 | SL9 | SL10 | Average Value |
---|---|---|---|---|---|---|---|
F1 | 0.1 | 32.45 | 29.17 | 29.94 | 30.17 | 30.74 | 30.49 |
0.3 | 58.51 | 55.56 | 56.83 | 47.00 | 52.13 | 54.01 | |
0.5 | 65.62 | 55.61 | 64.67 | 61.44 | 57.11 | 60.89 | |
0.7 | 49.00 | 46.05 | 47.95 | 43.26 | 41.46 | 45.54 | |
0.9 | 24.27 | 25.80 | 23.08 | 26.51 | 24.12 | 24.76 | |
IoU | 0.1 | 19.81 | 17.11 | 17.62 | 17.77 | 18.16 | 18.09 |
0.3 | 41.89 | 38.93 | 40.35 | 30.71 | 35.25 | 37.43 | |
0.5 | 53.25 | 47.25 | 50.94 | 48.92 | 46.80 | 49.43 | |
0.7 | 26.10 | 33.22 | 35.01 | 29.95 | 28.19 | 30.49 | |
0.9 | 14.98 | 16.24 | 14.28 | 16.53 | 14.89 | 15.38 |
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Share and Cite
Tang, S.; Jin, Z.; Zhang, Y.; Lu, J.; Li, H.; Yang, J. A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects. Processes 2023, 11, 2615. https://doi.org/10.3390/pr11092615
Tang S, Jin Z, Zhang Y, Lu J, Li H, Yang J. A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects. Processes. 2023; 11(9):2615. https://doi.org/10.3390/pr11092615
Chicago/Turabian StyleTang, Shancheng, Zicheng Jin, Ying Zhang, Jianhui Lu, Heng Li, and Jiqing Yang. 2023. "A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects" Processes 11, no. 9: 2615. https://doi.org/10.3390/pr11092615
APA StyleTang, S., Jin, Z., Zhang, Y., Lu, J., Li, H., & Yang, J. (2023). A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects. Processes, 11(9), 2615. https://doi.org/10.3390/pr11092615