Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection
Abstract
:1. Introduction
- We propose novel augmentation techniques and a framework for self-supervised learning aimed at addressing class imbalance in anomaly detection.
- Our adjacent augmentations generate synthetic anomalies with realistic contour distortions, enhancing the model’s learning process.
- We develop a contrastive learning framework that leverages characteristics from anomaly detection benchmark datasets, improving the overall effectiveness of anomaly detection models.
2. Related Work
2.1. MVTec-AD Dataset
2.2. Representative Anomaly Detection
2.3. Class Imbalance
2.4. SimCLR
3. Methods
3.1. Augmentation
3.1.1. Weak Overall
- The first step is to crop the anchor from 90% to 100% and then resize it to the size of the anchor.
- The second step is to adjust the brightness, contrast, saturation, and hue properties of the anchor to random values between 0% and 10%.
- The next step is to apply a Gaussian blur with a kernel size of 5 by 5 and a sigma value between 0.1 and 0.3.
- The final step is to apply a horizontal flip with random probabilities.
3.1.2. Strong Overall
- The first step is to crop the anchor to a random size and then resize it to the size of the anchor.
- The second step is to apply horizontal flipping with random probabilities.
- The next step is to adjust the brightness, contrast, and saturation properties of the anchor to random values between 0% and 80%, and the hue to random values between 0% and 20%.
- The random grayscale method converts images to black and white with a 20% probability.
- The final step is to apply a Gaussian blur using a kernel with a size of 10% of the anchor.
3.1.3. CutPaste
- The first step is to apply the Weak Overall augmentation.
- The second step is to set the size ratio of the patch to 2% to 15% and the aspect ratio to 0.3 to 3.
- The third step is to cut out the square patch from the anchor to the specified size.
- The final step is to paste the patch into a random location in the original image.
3.1.4. SmoothBlend
- The first step is to apply the Weak Overall augmentation.
- The second step is to set the size ratio of the patch to 0.5% to 1%, and the aspect ratio to 0.3 to 3.
- The third step is to cut out the round patch from the anchor to the specified size.
- The fourth step is to apply random contrast up to 100% to the patch, random saturation up to 100%, and random color jittering up to 50%.
- The final step is to alpha blend the original image and its patch.
3.1.5. Mosaic
- The first step is to apply the Weak Overall augmentation.
- The second step is to set the size ratio of the round area to be converted to 0.5% to 1%, and the aspect ratio to 1.
- The third step is to reduce the specified area to the rate of ζ and restore it to its original size.
- The fourth step is to apply random brightness up to 50%, random contrast up to 50%, random saturation up to 50%, and random color jittering up to 20%.
- The final step is to alpha blend the original image and the converted area.
3.1.6. Liquify
- The first step is to apply the Weak Overall augmentation.
- The second step is to assign a random point to the image.
- The third step specifies each coordinate of the four triangles centered around the designated point.
- The fourth step moves the specified point to a random location at a distance of the image size × (1/η)%.
- In the final step, four triangles move as the point moves, creating contour distortion.
3.1.7. Mosiquify
- The first step is to apply the Weak Overall augmentation.
- The second step is to apply the Mosaic (ζ = 20) augmentation.
- The final step is to apply the Liquify (η = 0.05) augmentation.
3.2. Adjacent Framework
Algorithm 1 Adjacent Framework’s main learning algorithm |
Input: batch size N, constant , structure of . for sampled minibatch do for all do draw three augmentation functions # anchor # the first augmentation(Weak Overall-positive) # the second augmentation(Strong Overall-positive) # the third augmentation(Liquify-negative) for all do define define update networks f and g to minimize end for end for return encoder network f(∙), and throw away g(∙) |
4. Experiments
5. Discussion
5.1. Summary of Findings
5.2. Comparison with Existing Methods
5.3. Impact of Deep Learning Architecture
5.4. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
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CutPaste [13] | SmoothBlend [14] | Mosaic | Liquify | Mosiquify | |
---|---|---|---|---|---|
Millisecond | 227.393 | 180.518 | 95.744 | 207.446 | 267.289 |
Category | #Train | #Test (Good) | #Test (Defect.) | #Defect Groups | #Defect Regions | Image Side Length | |
---|---|---|---|---|---|---|---|
Textures | Carpet | 280 | 28 | 89 | 5 | 97 | 1024 |
Grid | 264 | 21 | 57 | 5 | 170 | 1024 | |
Leather | 245 | 32 | 92 | 5 | 99 | 1024 | |
Tile | 230 | 33 | 84 | 5 | 86 | 840 | |
Wood | 247 | 19 | 60 | 5 | 168 | 1024 | |
Objects | Bottle | 209 | 20 | 63 | 3 | 68 | 900 |
Cable | 224 | 58 | 92 | 8 | 151 | 1024 | |
Capsule | 219 | 23 | 109 | 5 | 114 | 1000 | |
Hazelnut | 391 | 40 | 70 | 4 | 136 | 1024 | |
Metal Nut | 220 | 22 | 93 | 4 | 132 | 700 | |
Pill | 267 | 26 | 141 | 7 | 245 | 800 | |
Screw | 320 | 41 | 119 | 5 | 135 | 1024 | |
Toothb. | 60 | 12 | 30 | 1 | 66 | 1024 | |
Trans. | 213 | 60 | 40 | 4 | 44 | 1024 | |
Zipper | 240 | 32 | 119 | 7 | 177 | 1024 | |
Total | 3629 | 467 | 1258 | 73 | 1888 | - |
AU-ROC AU-PR | SimCLR [15] Mosaic ζ: 20 | A.F. Mosaic ζ: 20 | SimCLR [15] Liquify η: 0.05 | A.F. Liquify η: 0.05 | SimCLR [15] Mosiquify ζ = 20, η = 0.05 | A.F. Mosiquify ζ = 20, η = 0.05 |
---|---|---|---|---|---|---|
Framework and Aug. Category | ||||||
Zipper | 0.789391 0.928752 | 0.773897 0.924250 | 0.829832 0.948985 | 0.942752 0.985565 | 0.788340 0.933215 | 0.738183 0.925600 |
Hazelnut | 0.912143 0.954757 | 0.848929 0.911285 | 0.869286 0.932978 | 0.954286 0.975100 | 0.842143 0.908097 | 0.962500 0.979674 |
Bottle | 0.917460 0.975251 | 0.998413 0.999500 | 0.986508 0.995740 | 1.000000 1.000000 | 0.938095 0.980658 | 0.950794 0.981918 |
AU-ROC AU-PR | CutPaste [13] | SmoothBlend [14] | Mosaic ζ = 20 | Liquify | Mosiquify ζ = 20, η = 0.05 |
---|---|---|---|---|---|
Aug. Category | |||||
Leather | 0.713315 0.889585 | 0.830163 0.942820 | 0.853940 0.944785 | 0.906590 0.967224 η: 0.01 | 0.635870 0.860233 |
Zipper | 0.884979 0.964362 | 0.764968 0.929153 | 0.773897 0.924250 | 0.942752 0.985565 η: 0.05 | 0.738183 0.925600 |
Screw | 0.923140 0.974795 | 0.819840 0.932187 | 0.792785 0.919263 | 0.927649 0.975877 η: 0.1 | 0.724739 0.892260 |
Hazelnut | 0.863929 0.925698 | 0.916786 0.958774 | 0.848929 0.911285 | 0.954286 0.975100 η: 0.05 | 0.962500 0.979674 |
Tile | 0.871573 0.942547 | 0.823232 0.920362 | 0.936869 0.976487 | 0.876263 0.952038 η: 0.01 | 0.898990 0.964675 |
Transistor | 0.800417 0.763719 | 0.781250 0.706233 | 0.849167 0.813358 | 0.888750 0.877592 η: 0.1 | 0.772083 0.766140 |
Bottle | 0.929365 0.978421 | 0.973810 0.992562 | 0.998413 0.999500 | 1.000000 1.000000 η: 0.05 | 0.950794 0.981918 |
Metal nut | 0.886608 0.973357 | 0.804008 0.950171 | 0.838710 0.958632 | 0.927175 0.981952 η: 0.05 | 0.871457 0.966081 |
Toothbrush | 0.672222 0.854802 | 0.880556 0.952743 | 0.758333 0.899457 | 0.894444 0.958447 η: 0.01 | 0.819444 0.930904 |
Wood | 0.785088 0.927378 | 0.879825 0.964844 | 0.868421 0.959927 | 0.908772 0.971883 η: 0.1 | 0.858772 0.957199 |
AU-ROC AU-PR | None | Mosaic ζ = 20 | Liquify | Mosiquify ζ = 20, η = 0.05 |
---|---|---|---|---|
Neg. Sample Category | ||||
Leather | 0.697351 0.869883 | 0.853940 0.944785 | 0.906590 0.967224 η: 0.01 | 0.635870 0.860233 |
Zipper | 0.867122 0.965440 | 0.773897 0.924250 | 0.942752 0.985565 η: 0.05 | 0.738183 0.925600 |
Screw | 0.876819 0.961209 | 0.792785 0.919263 | 0.927649 0.975877 η: 0.1 | 0.724739 0.892260 |
Hazelnut | 0.925714 0.964005 | 0.848929 0.911285 | 0.954286 0.975100 η: 0.05 | 0.962500 0.979674 |
Tile | 0.797619 0.906408 | 0.936869 0.976487 | 0.876263 0.952038 η: 0.01 | 0.898990 0.964675 |
Transistor | 0.641250 0.543602 | 0.849167 0.813358 | 0.888750 0.877592 η: 0.1 | 0.772083 0.766140 |
Bottle | 0.869048 0.954794 | 0.998413 0.999500 | 1.000000 1.000000 η: 0.05 | 0.950794 0.981918 |
Metal Nut | 0.773216 0.940585 | 0.838710 0.958632 | 0.927175 0.981952 η: 0.05 | 0.871457 0.966081 |
Toothbrush | 0.830556 0.930351 | 0.758333 0.899457 | 0.894444 0.958447 η: 0.01 | 0.819444 0.930904 |
Wood | 0.715789 0.878648 | 0.868421 0.959927 | 0.908772 0.971883 η: 0.1 | 0.858772 0.957199 |
AU-ROC AU-PR | Liquify η: 0.01 | Liquify η: 0.03 | Liquify η: 0.05 | Liquify η: 0.1 |
---|---|---|---|---|
Liquify (η) Category | ||||
Leather | 0.906590 0.967224 | 0.866168 0.942729 | 0.861753 0.954633 | 0.791440 0.926733 |
Tile | 0.876263 0.952038 | 0.855700 0.944713 | 0.858225 0.951333 | 0.825758 0.927673 |
Toothbrush | 0.894444 0.958447 | 0.844444 0.943854 | 0.816667 0.930320 | 0.802778 0.914638 |
Zipper | 0.849002 0.953625 | 0.847952 0.951083 | 0.942752 0.985565 | 0.877363 0.954699 |
Hazelnut | 0.904286 0.943691 | 0.927143 0.958012 | 0.954286 0.975100 | 0.896071 0.941236 |
Carpet | 0.519663 0.836309 | 0.508026 0.812877 | 0.731942 0.919088 | 0.676164 0.895256 |
Bottle | 0.865873 0.952921 | 0.996825 0.999755 | 1.000000 1.000000 | 0.994444 0.998320 |
Metal Nut | 0.822092 0.956493 | 0.817204 0.952127 | 0.927175 0.981952 | 0.810850 0.949913 |
Cable | 0.794978 0.871893 | 0.846130 0.909616 | 0.802849 0.874685 | 0.847639 0.917611 |
Screw | 0.726173 0.893507 | 0.703423 0.884927 | 0.782332 0.917255 | 0.927649 0.975877 |
Pill | 0.633115 0.907203 | 0.729951 0.928439 | 0.655210 0.899663 | 0.735952 0.936548 |
Transistor | 0.844167 0.823265 | 0.834583 0.818871 | 0.815000 0.804524 | 0.888750 0.877592 |
Wood | 0.818421 0.939274 | 0.868421 0.960217 | 0.811404 0.942707 | 0.908772 0.971883 |
Grid | 0.765246 0.900952 | 0.794486 0.920962 | 0.782790 0.920473 | 0.812865 0.921557 |
Capsule | 0.735939 0.932523 | 0.768249 0.939492 | 0.799761 0.952384 | 0.814120 0.954620 |
AU-ROC AU-PR | CutPaste [13] | SmoothBlend [14] | Mosaic ζ = 20 | Liquify η: 0.01 | Mosiquify ζ = 20, η: 0.05 |
---|---|---|---|---|---|
Method Category | |||||
Pipe_fryum | 0.869000 0.926897 | 0.917400 0.955720 | 0.874600 0.940887 | 0.958800 0.979326 | 0.899800 0.953652 |
AU-ROC | Avg. | Bottle | Cable | Capsule | Carpet | Grid | Hazeln. | Leather |
---|---|---|---|---|---|---|---|---|
Category Method | ||||||||
GeoTrans [31] | 67.2 | 74.4 | 78.3 | 67.0 | 43.7 | 61.9 | 35.9 | 84.1 |
GANomaly [32] | 76.2 | 89.2 | 75.7 | 73.2 | 69.9 | 70.8 | 78.5 | 84.2 |
SPADE [33] | 85.5 | - | - | - | - | - | - | - |
Liquify | 88.3 | 100 η: 0.05 | 84.8 η: 0.1 | 88.6 η: 0.03 | 73.2 η: 0.05 | 81.3 η: 0.1 | 95.4 η: 0.05 | 90.7 η: 0.01 |
PaDiM [26] | 95.3 | - | - | - | - | - | - | - |
PatchCore [27] | 99.1 | 100 | 99.5 | 98.1 | 98.7 | 98.2 | 100 | 100 |
AU-ROC | Metal nut | Pill | Screw | Tile | Toothb. | Trans. | Wood | Zipper |
Category Method | ||||||||
GeoTrans [31] | 81.3 | 63.0 | 50.0 | 41.7 | 97.2 | 86.9 | 61.1 | 82.0 |
GANomaly [32] | 70.0 | 74.3 | 74.6 | 79.4 | 65.3 | 79.2 | 83.4 | 74.5 |
SPADE [33] | - | - | - | - | - | - | - | - |
Liquify | 92.7 η: 0.05 | 73.6 η: 0.1 | 92.8 η: 0.1 | 87.6 η: 0.01 | 89.4 η: 0.01 | 88.9 η: 0.1 | 90.9 η: 0.1 | 94.3 η: 0.05 |
PaDiM [26] | - | - | - | - | - | - | - | - |
PatchCore [27] | 100 | 96.6 | 98.1 | 98.7 | 100 | 100 | 99.2 | 99.4 |
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Kwon, G.S.; Choi, Y.S. Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection. Sensors 2024, 24, 5616. https://doi.org/10.3390/s24175616
Kwon GS, Choi YS. Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection. Sensors. 2024; 24(17):5616. https://doi.org/10.3390/s24175616
Chicago/Turabian StyleKwon, Gi Seung, and Yong Suk Choi. 2024. "Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection" Sensors 24, no. 17: 5616. https://doi.org/10.3390/s24175616