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README.md

Weld Porosity Detection

Weld Porosity Detection prevents defects in real time using AI-powered monitoring. AI and machine vision enable real-time detection of welding defects, ensuring immediate corrective action before issues escalate. Using the right camera and computing hardware, a trained AI model continuously monitors the welds, halting the process the moment a defect is detected. Deep learning AI processes video data at frame rates far beyond human capability, delivering unmatched precision and reliability.

It is a cloud-native application composed of microservices, using pre-trained deep learning models for video analysis. This sample application offers the following features:

  • High-speed data exchange with low-latency compute.
  • AI-assisted defect detection in real-time as pallets are received at the warehouse.
  • On-premise data processing for data privacy and efficient use of bandwidth.
  • Interconnected warehouses deliver analytics for quick and informed tracking and decision making.

Get Started

To see the system requirements and other installation, see the following guides:

How It Works

You can read the overview of the architecture and logic of the application

The components and services are as follows:

  • DL Streamer Pipeline Server is a core component of the app. It receives video feed from multiple cameras (four by default, simulated with a video recording). With pre-trained deep learning models, it performs real-time object detection, classification, and tracking. It recognizes vehicles in the parking lot and sends their 2D bounding boxes to Node-Red, through the MQTT Broker. It also adds the detected bounding boxes on top of the video input, consumed by the WebRTC Server.
  • Mosquitto MQTT Broker is a message distribution service, passing data between these sends the raw coordinates of detected vehicles to Node-RED. The feedback it receives is moved to Grafana to display.
  • WebRTC Server serves video streams processed by the pipeline for end-user visualization. It is supplemented by the Coturn signaling server and passes the feed for display in Grafana.

It also consists of the following third-party microservices:

  • Nginx is a high-performance web server and reverse proxy that provides TLS termination and unified HTTPS access.
  • MediaMTX Server is a real-time media server and media proxy that allows publishing webrtc stream.
  • Coturn Server is a media traffic NAT traversal server and a gateway.
  • Open telemetry Collector is a set of receivers, exporters, processors, connectors for Open Telemetry.
  • Prometheus is a systems and service monitoring system used for viewing Open Telemetry.
  • Postgres is an object-relational database system that provides reliability and data integrity.
  • Minio is a high performance object storage that is API compatible with Amazon S3 cloud storage service.