MultiModal Weld Defect Detection
================================

The MultiModal Weld Defect Detection sample demonstrates how to use AI at the edge to identify weld
defects in manufacturing environments by analyzing both image and time series sensor data.

In industrial settings, weld quality is critical for safety and reliability. Manual inspection is time-consuming
and prone to human error. This solution leverages deep learning models to automate defect detection, improving accuracy
and efficiency.

Key features include:

- Multi-modal data fusion: Combines visual inspection (images) and sensor data (such as current, voltage, and temperature)
  for comprehensive defect detection.
- Real-time inference: Processes data at the edge for immediate feedback and reduced latency.
- Configurable alerts: Notifies operators of detected defects to enable timely intervention.
- Extensible pipeline: Supports integration with additional data sources and models.

This application helps manufacturers enhance product quality, reduce inspection time, and minimize costly
rework by enabling proactive defect detection on the factory floor.

.. toctree::
   :hidden:

   how-it-works
   system-requirements
   get-started
   how-to-build-from-source
   how-to-deploy-with-helm
   how-to-configure-alerts
   how-to-update-config
   release_notes/Overview