QuantEye-Visual Difference Engine

Automating the Next Generation of High-Speed Visual Analysis.

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QuantEye

Planned Visual Change Detection System for Haas F1 Team and Industrial Applications

Project Proposal / Technical Plan

Overview

QuantEye is a planned general-purpose AI system designed to detect, classify, and quantify visual changes across time-series imagery.
Unlike simple pixel-difference algorithms, QuantEye will leverage deep learning to identify meaningful, context-aware changes, distinguishing between lighting variations and actual material or structural alterations.

The system is intended to analyze multiple imaging modalities of the same scene, including standard RGB, infrared (IR), and thermal visuals.
By integrating these complementary perspectives, QuantEye will be able to detect subtle variations invisible in a single modality — such as heat anomalies, subsurface defects, or early material fatigue.
This multimodal approach is expected to provide a richer, more reliable understanding of visual changes across diverse environments.

Applications

QuantEye is initially planned with a focus on the Haas F1 Team, supporting high-precision visual analysis in motorsport engineering, while remaining adaptable for broader industrial and infrastructure use.

  • Race Car Component Monitoring:
    Detect micro-wear, delamination, or structural fatigue across carbon fiber components, suspension arms, and aerodynamic surfaces by comparing high-resolution image sequences before and after races or wind tunnel sessions.

  • Thermal and Infrared Analysis:
    Use IR and thermal imagery to identify overheating regions, energy loss, or hidden stress points on power units, brakes, and exhaust systems.

  • Pit Equipment and Garage Compliance:
    Ensure visual verification of pit lane tools, garage layouts, and branded equipment according to FIA and sponsor standards.

  • Manufacturing and Fabrication Quality Control:
    Track production consistency across composite fabrication, paint finishes, and part assembly within Haas F1’s workflows.

  • Infrastructure and Facility Monitoring:
    Apply visual intelligence to monitor long-term infrastructure such as team facilities, transport equipment, or trackside installations — detecting corrosion, misalignment, or material wear.

Input Modalities

The project will explore multiple imaging modalities to improve detection accuracy:

  • RGB (Standard): High-resolution visual inspection.
  • Infrared / Thermal: Detect heat patterns, energy leaks, and material stress invisible in RGB.
  • Depth / LiDAR: Optional 3D surface monitoring for deformation tracking.
  • Synthetic / Simulated Data: Use Blender or Unreal Engine to create controlled examples of wear, corrosion, or structural defects for model training.

Training Data Strategy

1. Real-World Data Acquisition

  • Capture time-series images from race cars, pit equipment, and manufacturing setups.
  • Collect multimodal data (RGB, thermal, IR) under various operating conditions.
  • Partner with Haas F1 Team engineers to annotate component-level changes and failures.

2. Synthetic Data Generation

  • Generate controlled synthetic imagery simulating wear, corrosion, scratches, and paint defects.
  • Vary lighting, angles, and materials to improve robustness of the model.
  • Use domain adaptation to bridge the gap between simulated and real imagery.

3. Data Labeling

Each change event will be annotated with:

  • Type: defect, corrosion, contamination, missing part, deformation.
  • Severity: minor, moderate, critical.
  • Region of Interest (ROI).
  • Time Interval: ∆t between frames.

Planned Model Architecture

QuantEye will combine feature extraction, difference computation, segmentation, and classification:

  • Backbone: Vision Transformer (ViT) or ConvNeXt for deep feature extraction.
  • Change Head: Siamese or Triplet network to compare image embeddings.
  • Segmentation Decoder: U-Net or Mask2Former to localize changes.
  • Classifier: Softmax head for change-type classification.

Processing Pipeline

The QuantEye system processes visual data through the following stages:

  • Input Images:
    Receive two or more time-series images — Image(t1) and Image(t2).

  • Feature Extraction:
    Use a deep learning backbone (e.g., Vision Transformer or ConvNeXt) to extract visual features from each image.

  • Feature Differencing:
    Compute the difference between feature embeddings to detect potential changes.

  • Segmentation Mask Generation:
    Apply a segmentation decoder (e.g., U-Net or Mask2Former) to localize areas of change.

  • Classification:
    Use a Softmax classifier to categorize the type of change (e.g., defect, corrosion, deformation).

Overall Flow:
Image(t1) → Image(t2) → FeatureExtractor → FeatureDiff → SegmentationMask → Classification

Evaluation Metrics

  • Change Detection: F1-score, Precision, Recall, IoU.
  • Localization Accuracy: Dice Coefficient.
  • Classification Quality: Accuracy, Macro-F1.
  • Robustness: Performance under lighting, noise, and angle variation.

Deployment and Integration

  • Lightweight versions on edge devices for real-time pit and garage monitoring.
  • Cloud-based inference for batch analysis and trend monitoring.
  • REST APIs for integration with existing inspection, telemetry, or compliance systems.

Future Extensions

  • Temporal trend analysis to predict component degradation over time.
  • Unsupervised change detection for zero-shot adaptation to new car parts or facilities.
  • Explainable AI overlays to highlight why and where changes are detected.

Conclusion

QuantEye is a proposed visual intelligence system designed to support the Haas F1 Team with high-precision, multimodal visual change detection, while remaining adaptable for broader industrial and infrastructure applications.
By leveraging a combination of real, synthetic, and multimodal data, the project aims to deliver actionable visual insights that improve reliability, safety, and performance monitoring.

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