Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
Abstract
:1. Introduction
- We have designed a new unsupervised anomaly detection framework, FEPDM, which reduces bias between the source and target domains while significantly decreasing the number of model training parameters by conducting training at the feature level.
- We have introduced a novel anomaly simulation strategy for self-supervised learning of the model. This strategy generates anomaly blocks with diverse textures and structures, closely resembling real-world anomalies.
- We have elaborated a multi-scale feature aggregation module capable of integrating multi-resolution, multi-semantic features from different network hierarchies. This module effectively reduces the dimensionality of these features to the target dimensions through an efficient dimensionality reduction strategy.
- Extensive experiments on today’s most challenging real-world datasets, such as MVTec and BTAD, demonstrate that our approach exhibits excellent performance and generalization.
2. Related Work
2.1. Reconstruction-Based Approach
2.2. Embedding-Based Approach
2.3. Self-Supervised Learning Based Approach
3. Materials and Methods
3.1. Pseudo-Anomaly Image and Label Generation
- (a)
- A random two-dimensional Perlin noise P is first generated and randomly rotated to increase diversity. Subsequently, the noise is binarized using a set threshold to obtain the original anomaly mask . For the normal image I, we obtain the foreground mask of the input image through threshold binarization. This ensures that our simulated anomalies are generated on the detected target rather than in the background, avoiding unnecessary noise. The simulated anomaly mask M is obtained by multiplying the foreground mask with the Perlin mask, as shown in Figure 2a.
- (b)
- The foreground of the anomaly block is obtained by multiplying the anomaly source image and the simulated anomaly mask M on an element-by-element multiplication:
- (c)
- Multiply the input image by the simulated anomaly inverse mask to get the image without anomalous blocks (Figure 2b), and finally add it to the anomalous block mask to obtain the final pseudo-anomalous image .
3.2. Multi-Scale Feature Aggregation
3.3. Self-Supervised Training
3.4. Calculate Anomaly Score Map
3.5. Datasets
4. Results
4.1. Experimental Details
4.2. Experimental Results
4.2.1. Evaluation Metrics
4.2.2. Image-Level Anomaly Detection
4.2.3. Pixel-Level Anomaly Location
4.3. Ablation Experiment
4.3.1. Features Layer Selection
4.3.2. Attention Mechanisms
4.3.3. Dimensional Ablation
4.3.4. Different Anomaly Simulation Strategies
4.4. Discussions
4.4.1. Computational Complexity and Memory Complexity
4.4.2. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Structure | Input | Output | Channel |
---|---|---|---|
Conv_block1 | 56 × 56 | 56 × 56 | 128 |
Avgpool1 | 56 × 56 | 28 × 28 | 128 |
Conv_block2 | 28 × 28 | 28 × 28 | 256 |
Avgpool2 | 28 × 28 | 14 × 14 | 256 |
Conv_block3 | 14 × 14 | 14 × 14 | 512 |
Avgpool3 | 14 × 14 | 7 × 7 | 512 |
AdaptiveAvgPool | 7 × 7 | 1 × 1 | 512 |
MLP | [512, 512, 128] | ||
FC | 3 |
Category | SPADE [5] | RIAD [15] | P-SVDD [31] | P-SVDD-C [32] | PaDiM [6] | CutPaste [10] | SCGAN [19] | Ours | |
---|---|---|---|---|---|---|---|---|---|
Carpet | 92.8 | 84.2 | 92.9 | 94.4 | 99.9 | 93.1 | 97.0 | 98.9 | |
Grid | 47.3 | 99.6 | 94.6 | 95.6 | 95.7 | 99.9 | 96.3 | 96.9 | |
Texture | Leather | 61.5 | 100.0 | 90.9 | 96.1 | 100.0 | 100.0 | 94.7 | 100.0 |
Tile | 96.5 | 98.7 | 97.8 | 93.5 | 97.4 | 93.4 | 97.4 | 97.3 | |
Wood | 95.8 | 93.0 | 96.5 | 98.0 | 98.8 | 98.6 | 100.0 | 97.8 | |
Average | 85.6 | 95.1 | 94.5 | 95.5 | 98.4 | 97.0 | 97.1 | 98.2 | |
Bottle | 97.2 | 99.9 | 98.6 | 99.5 | 99.8 | 98.3 | 98.3 | 99.9 | |
Cable | 84.8 | 81.9 | 90.3 | 97.8 | 92.2 | 80.6 | 98.2 | 94.9 | |
Capsule | 89.7 | 88.4 | 76.7 | 88.7 | 91.5 | 96.2 | 83.2 | 90.3 | |
Hazelnut | 88.1 | 83.3 | 92.0 | 97.9 | 93.3 | 97.3 | 97.5 | 94.2 | |
Object | Metal nut | 71.0 | 88.5 | 94.0 | 96.5 | 99.2 | 99.3 | 90.1 | 99.1 |
Pill | 80.1 | 83.8 | 86.1 | 91.9 | 94.4 | 92.4 | 89.4 | 89.7 | |
Screw | 66.7 | 84.5 | 81.3 | 83.3 | 84.4 | 86.3 | 100.0 | 87.5 | |
Toothbrush | 88.9 | 100.0 | 100.0 | 95.6 | 97.2 | 98.3 | 100.0 | 94.7 | |
Transistor | 90.3 | 90.9 | 91.5 | 92.1 | 97.8 | 95.5 | 91.3 | 95.7 | |
Zipper | 96.6 | 98.1 | 97.9 | 95.9 | 90.9 | 99.4 | 92.4 | 98.2 | |
Average | 85.3 | 89.9 | 90.8 | 93.9 | 94.1 | 94.3 | 94.0 | 93.9 | |
Average | 85.4 | 91.7 | 92.1 | 94.4 | 95.5 | 95.2 | 95.1 | 95.7 |
Category | SPADE [5] | RIAD [15] | P-SVDD [31] | P-SVDD-C [32] | PaDiM [6] | CutPaste [10] | Ours | |
---|---|---|---|---|---|---|---|---|
Carpet | 97.5 | 96.3 | 92.6 | 92.9 | 98.8 | 98.3 | 98.9 | |
Grid | 93.7 | 98.8 | 96.2 | 97.2 | 93.6 | 97.5 | 94.0 | |
Texture | Leather | 97.6 | 99.4 | 97.4 | 98.2 | 99.0 | 99.5 | 99.2 |
Tile | 87.4 | 89.1 | 91.4 | 91.9 | 91.7 | 90.5 | 94.0 | |
Wood | 85.5 | 85.8 | 90.8 | 92.1 | 94.0 | 95.5 | 91.6 | |
Average | 92.3 | 93.9 | 93.7 | 94.5 | 95.3 | 96.3 | 95.5 | |
Bottle | 98.4 | 98.4 | 98.1 | 98.6 | 98.1 | 97.6 | 97.3 | |
Cable | 97.2 | 84.2 | 96.8 | 97.6 | 94.9 | 90.0 | 96.0 | |
Capsule | 99.0 | 92.8 | 95.8 | 96.3 | 98.2 | 97.4 | 97.5 | |
Hazelnut | 99.1 | 96.1 | 97.5 | 98.2 | 97.9 | 97.3 | 96.6 | |
Object | Metal nut | 98.1 | 92.5 | 98.0 | 98.1 | 96.7 | 93.1 | 94.1 |
Pill | 96.5 | 95.7 | 95.1 | 92.4 | 94.6 | 95.7 | 95.7 | |
Screw | 98.9 | 98.8 | 95.7 | 95.3 | 97.2 | 96.7 | 96.7 | |
Toothbrush | 97.9 | 98.9 | 98.1 | 96.0 | 98.6 | 98.1 | 97.2 | |
Transistor | 94.1 | 87.7 | 97.0 | 93.5 | 96.8 | 93.0 | 96.4 | |
Zipper | 96.5 | 97.8 | 95.1 | 96.0 | 97.6 | 99.3 | 97.9 | |
Average | 97.6 | 94.3 | 96.7 | 96.2 | 97.1 | 95.8 | 96.5 | |
Average | 96.0 | 94.2 | 95.7 | 95.6 | 96.5 | 96.0 | 96.2 |
Category | SPADE [5] | P-SVDD [31] | PatchCore [7] | VT-ADL [46] | Ours |
---|---|---|---|---|---|
01 | (91.4, 97.3) | (95.7, 91.6) | (90.9, 95.5) | (-, 99.0) | (97.3, 97.0) |
02 | (71.4, 94,4) | (72.1, 93.6) | (79.3, 94.7) | (-, 94.0) | (84.1, 96.6) |
03 | (99.9, 99.1) | (82.1, 91.0) | (99.8, 99.3) | (-, 77.0) | (98.7, 99.2) |
Average | (87.6, 96.9) | (83.3, 92.1) | (90.0, 96.5) | (-, 90.0) | (93.4, 97.6) |
Layer 1 | Layer 2 | Layer 3 | I-AUROC | P-AUROC |
---|---|---|---|---|
√ | 89.1 | 94.5 | ||
√ | 95.4 | 94.9 | ||
√ | 95.7 | 94.2 | ||
√ | √ | 93.2 | 95.3 | |
√ | √ | 95.4 | 95.8 | |
√ | √ | 95.6 | 95.6 | |
√ | √ | √ | 95.7 | 96.2 |
Category | w/o Atten | +SE | +CBAM | +CA |
---|---|---|---|---|
Texture | (97.8, 95.1) | (97.3, 94.6) | (96.5, 94.7) | (98.2, 95.5) |
Object | (93.9, 96.0) | (93.2, 96.2) | (93.4, 96.3) | (93.9, 96.5) |
Average | (95.2, 95.7) | (94.6, 95.7) | (94.4, 95.7) | (95.7, 96.2) |
Component | 256d | 200d | 160d | 100d |
---|---|---|---|---|
+SE | (95.4, 96.2) | (94.4, 95.9) | (95.2, 96.0) | (94.6, 95.7) |
+CBAM | (95.2, 96.4) | (94.9, 96.3) | (94.6, 96.3) | (94.4, 95.7) |
+CA | (95.1, 96.4) | (95.5, 96.6) | (94.9, 96.2) | (95.7, 96.2) |
Category | Rect | Scar | Rect & Scar | 2-Way | w/o Mask | Mask |
---|---|---|---|---|---|---|
Texture | (97.8, 95.3) | (98.4, 95.5) | (98.3, 95.6) | (97.7, 94.9) | (98.7, 96.7) | (98.2, 95.5) |
Object | (91.4, 95.7) | (91.5, 95.4) | (92.7, 95.6) | (92.9, 95.9) | (92.4, 96.3) | (93.9, 96.5) |
Average | (93.5, 95.6) | (93.8, 95.5) | (94.6, 95.6) | (94.5, 95.6) | (94.5, 96.4) | (95.7, 96.2) |
Models | I-AUROC | P-AUROC | Memory Bank (GB) | Parameters (M) |
---|---|---|---|---|
PaDiM (R18-RD100) | 90.5 | 96.5 | 0.12 | — |
PaDiM (R18-Full) | 93.9 | 97.1 | 2.3 | — |
PaDiM (WR50-RD550) | 95.5 | 97.3 | 3.5 | — |
CutPaste | 95.2 | 96.0 | — | 11.8 |
Ours | 95.7 | 96.2 | 0.12 | 3.8 |
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Wu, B.; Wang, X. Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance. Appl. Sci. 2024, 14, 7301. https://doi.org/10.3390/app14167301
Wu B, Wang X. Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance. Applied Sciences. 2024; 14(16):7301. https://doi.org/10.3390/app14167301
Chicago/Turabian StyleWu, Bin, and Xiaoqi Wang. 2024. "Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance" Applied Sciences 14, no. 16: 7301. https://doi.org/10.3390/app14167301