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Industrial Anomaly Detection Project Plan & Roadmap

Industrial Anomaly Detection: Strategic Plan & Roadmap

This master roadmap establishes the engineering milestones, verification gates, and software delivery phases for the Industrial Component Anomaly Detection project. Execution tracks are optimized to map directly to the Multi-Agent Governance Network and local just task infrastructure.

📅 High-Level Timeline & Milestones

[02/07] Scoping & Onboarding ── Completed
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[09/07] Data Deep Dive Sync ── Next Milestone (1:00 PM Thursday Sync)
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[17/07] Step 1: Data Exploration & 5x DataViz Complete
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[24/07] Step 2: Pre-processing & Feature Engineering ── DELIVERABLE 1 DUE (Midnight)
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[31/07] Step 3 (Phase 1): Baseline Modeling & Approach Evaluation
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[28/08] Step 3 (Phase 2): Advanced Evaluation, Metrics & Hyper-Optimization
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[04/09] Step 3 (Phase 3): Deep Learning, Bagging/Boosting & Interpretability ── DELIVERABLE 2 DUE
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[15/09] Step 4: Final Combined Synthesis Report Submission
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[22/09] Step 5: Streamlit Application Deployment & Jury Defense

🛠️ Detailed Operational Breakdown

Phase 1: Exploration, Visual Verification & Structural Analysis

Timeline: 03/07 to 17/07

Core Objective: Complete deep structural review of the dataset (MVTec AD) utilizing OpenCV and native plotting matrices. Identify data anomalies, class imbalances, and lighting variances.

  • Thursday Sync (July 9 @ 1:00 PM): Group meeting to evaluate raw images together, flag format discrepancies, and outline specific data contamination risks.
  • The 5-Graph Visual Gauntlet Requirements:

Each visualization staged in notebooks/ must contain an accompanying business impact commentary and a data manipulation or statistical validation gate:

  1. Class Imbalance Profile: Ratio of Normal to specific Anomaly sub-classes across component categories. Statistical Validation: Chi-Square goodness-of-fit test against a uniform distribution.
  2. Spatial Anomaly Heatmap: Coordinate mapping of defect locations across image matrices to establish spatial distribution priors. Statistical Validation: Peak density measurement vs. spatial random uniform distribution.
  3. Color Channel & Pixel Intensity Histograms: Comparison of anomalous regions against corresponding golden templates. Statistical Validation: Two-sample Kolmogorov-Smirnov test on intensity variances.
  4. Aspect Ratio & Scale Sizing Distribution: Scatter plot of structural defect bounds relative to complete component boundaries. Statistical Validation: Pearson/Spearman rank correlation coefficients for dimension drift.
  5. Latent Feature Visual Clustering: t-SNE or UMAP projection of embeddings extracted from a pre-trained back-bone (e.g., ResNet) to confirm baseline linear/non-linear separability. Statistical Validation: Silhouette score evaluation across latent clusters.

Phase 2: Pre-processing & Hardened Pipelines

Timeline: 18/07 to 24/07

Core Objective: Construct robust image processing steps to feed the deep modeling loops.

  • Implementation Steps:
  • Image resizing, standardization, and brightness normalization loops using OpenCV.
  • Augmentation profiling (Albumentations) carefully tuned to avoid generating unrealistic artifact errors (e.g., flipping a directional industrial component incorrectly).

  • Splitting data matrices into deterministic, seed-locked Train/Validation/Test sets.

  • 📬 DELIVERABLE 1 GATE: Submit the complete Exploration, DataViz, and Pre-processing Report to Slack on July 24 before midnight.

Phase 3: Iterative Modeling & Evaluation Engine

Timeline: 25/07 to 04/09

  • Milestone 3.1: Baseline Models (Deadline: 31/07)
  • Scaffold simple architectures (e.g., a standard multi-layer CNN or structural autoencoder for unsupervised reconstruction).

  • Establish performance baselines on the binary classification layer.

  • Milestone 3.2: Metric Optimization & Exploitation (Deadline: 28/08)

  • Evaluate Precision, Recall, F1-Score, and ROC-AUC metrics. For industrial parts, heavily prioritize mitigating False Negatives (missed defects), mapping this to the business cost of equipment failure.

  • Integrate optimization tools (such as Optuna) to search hyperparameter spaces for layer dimensions, learning rates, and weight decays.

  • Milestone 3.3: Deep Learning, Ensembles & Explainability (Deadline: 04/09)

  • Deploy modern anomaly detection architectures (such as PatchCore, FastFlow, or advanced deep Siamese Networks).
  • Incorporate bagging/boosting protocols where feature matrices can be aggregated.

  • Explainable AI (XAI Gate): Utilize interpretability tools (like Grad-CAM) to plot activation maps directly over the images, proving that the model is making decisions based on actual mechanical defects rather than background noise.

  • 📬 DELIVERABLE 2 GATE: Submit the comprehensive Modeling Report on September 4.

Phase 4: Final Synthesis & Deployment Layout

Timeline: 05/09 to 22/09

  • Final Report (Deadline: 15/09): Compile Deliverables 1 and 2, adding global technical conclusions and architectural recommendations for live assembly line monitoring.
  • Streamlit & Defense System (Deadline: 22/09): Build a highly reactive Streamlit application hooked into the pipelines inside app/pipelines/. It must allow real-time image uploads, output binary sorting metrics, highlight defect locations using Grad-CAM, and output multi-class labels identifying defect types.
  • Defense Parameters: 20 minutes of technical presentation followed by a 10-minute Q&A panel session.

🤖 Multi-Agent Task Allocations for Project Track

When using autonomous IDE workflows, assign tasks to your agent profiles based on their defined rules to ensure zero-bypass safety:

  • Lead Orchestrator (01-orchestrator.md): Use to break down weekly goals into clear engineering specs and manage OKF node relationships across documentation.
  • Core Software Engineer (03-engineer.md): Delegate the creation of OpenCV pre-processing transformations, custom PyTorch dataset loaders, and model pipeline scripts within app/pipelines/anomaly_binary/ and app/pipelines/anomaly_classification/.
  • QA Automation Engineer (04-qa-automator.md): Assign to write deterministic assertions validating tensor shapes, data split leakage protections, and input format boundary checks in tests/.