AutoWeld-Vision: Unsupervised Welding Defect Detection

🚀 Live Demo: Interactive Operator Terminal

This project is an unsupervised late-fusion anomaly detection pipeline written in PyTorch. I built it to inspect visual weld seams for surface defects (like porosity, cracks, and voids) in real-time, while automatically logging signed quality reports to satisfy IATF 16949 section 8.5.2.1 compliance.

Rather than just editing YAML files and writing thin wrappers around Anomalib, I spent my time implementing the core algorithms—including intermediate feature hooks, coreset minimax compression, and Platt scaling probability calibration—from scratch in pure PyTorch.

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1. System Pipeline

  [ Input Optical Scan ] ---> [ CLAHE block-level histogram + smoothing ]
                                              |
                                              v
                                    [ ResNet Backbone ]
                                   /                 \
                                  /                   \
            [ Custom PatchCore Model ]          [ Custom Student-Teacher ]
            - layer2 & layer3 hook maps         - Randomly initialized student
            - Local avg pooling pooling         - Frozen teacher network
            - Coreset (capped at 1500)          - Autoencoder reconstruction
                                  \                   /
                                   \                 /
                                    v               v
                        [ Platt Scaling Sigmoid Calibrators ]
                        - Sigmoid: P(y=1 | S) = 1 / (1 + e^(A*S + B))
                                              |
                                              v
                        [ SLSQP Late-Fusion Weight Solver ]
                        - Scipy optimize BCE over calibrated probabilities
                                              |
                                              v
                        [ Quality Logger ] + [ Loopback Socket PLC Link ]

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2. Core Modules

2.1 Pure PyTorch PatchCore (autoweld_vision/models/patchcore.py)

• Forward Hooks: Dynamically hooks layer activations from layer2 and layer3 of frozen ImageNet-pretrained backbones.
• Context Pooling: Applies 3x3 local average pooling to concatenate multi-scale structural details.
• Minimax Coreset Selection: Implements the greedy search algorithm to shrink memory bank overhead. I capped this at a maximum of 1500 patch vectors to prevent out-of-memory crashes and maintain a sub-40ms execution loop.
• K-NN Scaling: Computes L2 distances against the coreset, scaling the final score by the density of the 9 nearest neighbors.

2.2 Calibrated Late-Fusion (autoweld_vision/models/ensemble.py)

• The Problem: Raw scores from PatchCore and student-teacher distillation represent completely different Euclidean scales. Adding them directly is statistically invalid.
• The Solution: We fit a logistic sigmoid ($P(y=1 \mid S) = 1 / (1 + e^{A \cdot S + B})$) for each model using Nelder-Mead optimization on validation splits to calibrate distances into probabilities.
• Weight Optimization: We solve for fusion weights ($w_1, w_2$) using Scipy's Sequential Least Squares Programming (SLSQP) solver to minimize Binary Cross-Entropy loss over calibrated probabilities.

2.3 Corrected DINOv2 Baseline (autoweld_vision/models/dinomaly.py)

• Standard anomaly baselines are often broken because they measure spatial contrast against the test image's own mean, which represents an incorrect spatial comparison.
• We cache a global reference normal mean feature map during the training fit phase. During testing, we compute L2 spatial discrepancies relative to this global baseline.

2.4 Integrity Auditing & Signaling (autoweld_vision/utils/quality.py)

• HMAC Signatures: Visual report metadata (VIN, timestamps, scores, decisions) are cryptographically signed using HMAC-SHA256, loading keys from AUTOWELD_SECRET_KEY env parameters to avoid cleartext exposure.
• PLC Socket: Emits Pass/Fail indicators (0x00/0x01) over TCP port 5002. It uses a strict 50ms connection timeout to prevent camera frame capture freezes if the receiving industrial controller is offline.

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3. Real Benchmark Results (MVTec AD)

The following metrics are the actual results generated by running the benchmark pipeline locally.

Product Category	Model / Pipeline	Image-Level AUROC	Pixel-Level AUROC	Inference Latency
Bottle	Scratch PatchCore (WideResNet-50)	100.0%	98.15%	20 ms
	Custom Student-Teacher (EfficientAD)	44.76%	96.70%	15 ms
	Calibrated Late-Fusion	100.0%	98.40%	35 ms
Cable	Scratch PatchCore (WideResNet-50)	98.69%	98.05%	22 ms
Metal Nut	Scratch PatchCore (WideResNet-50)	99.76%	98.20%	21 ms

Empirical Insights on Anomaly Convergence

• The Student-Teacher Bottleneck: The custom student-teacher (EfficientAD) model achieved an excellent 96.7% Pixel-Level AUROC on Bottle, showing that it accurately isolates anomalous shapes in local patches. However, its Image-Level AUROC was only 44.76% (worse than random guessing). This represents a representation collapse during early training: because the student was initialized from scratch and trained for only 3 epochs on normal data without complex synthetic anomalies or teacher projections, its global maximum discrepancy was noisy and unstable.
• Late Fusion Safeguard: Thanks to Platt Scaling, late fusion successfully balanced the models. It assigned a weight of 0.474 to PatchCore and 0.526 to EfficientAD, stabilizing the joint Image AUROC back to 100.0% and yielding a fused Pixel AUROC of 98.40%.
• Data Leakage Limitation: The benchmark runner currently fits the Platt Sigmoids and SLSQP weights on the test loader split due to dataset size constraints. This is a validation bottleneck that can cause overfitting, and in production, it must be optimized on a separate, held-out validation cohort.

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4. Setup and Usage

Setup

# Clone
git clone https://github.com/shaikhadibbb/AutoWeld-Vision.git
cd AutoWeld-Vision

# Environment
python3 -m venv venv
source venv/bin/activate

# Install requirements
pip install -r requirements-standard.txt

Run Benchmark

Train models, fit the coresets, calibrate Platt sigmoids, and dump results:

python scripts/run_benchmark.py --categories bottle

Single Image Audit

Inspect a single image, run inference, and compile cryptographically signed visual ledgers:

python test_inspection.py --image test_weld.png --vin WBA-BMW-2026 --category bottle

Streamlit Terminal

Run the interactive operator dashboard:

streamlit run app.py

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5. Real-World Manufacturing Challenges

1. Bare Aluminum Glare: Metallic surfaces behave like mirrors. Glares produce massive high-frequency activations that trick distillation models into flagging normal boundaries as anomalous. I lowered the CLAHE contrast clip parameter from $4.0$ to $2.0$ to avoid pixel clipping and added a small spatial Gaussian filter to smooth remaining specular edges.
2. Hidden Voids: Camera-based models only capture surface defects. Internal air pockets or lack of penetration depth are invisible to optical sensors. An industrial setup requires training these models on radiographic X-ray datasets (such as GDXray).
3. Cold Boot Lag: Loading massive Wide-ResNet weights and fitting coresets on startup introduces a delay of about 8 seconds before the inspection loop goes live.

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6. References & Citations

If you utilize this pipeline or scratch implementations in your research or industrial audits, please cite the original paradigms:

1. PatchCore (Total Recall): Roth, K., Pemberton, L., Zhang, M., Cherian, A., Nixon, T., & Harada, T. (2022). Towards Total Recall in Industrial Anomaly Detection. IEEE/CVF CVPR, 14318-14328.
2. EfficientAD (Student-Teacher): Batzner, K., Heckler, L., & König, R. (2024). EfficientAD: Accurate, Real-Time Anomaly Detection in Images. ICML.
3. MVTec AD Dataset: Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). MVTec AD -- A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. IEEE/CVF CVPR, 9592-9600.
4. Platt Scaling Calibration: Platt, J. (1999). Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers, 61-74.