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Casting Defect Detection using Computer Vision

This application demonstrates the usage of the Kelvin SDK to implement a solution to detect manufacturing defects using computer vision and machine learning.

Code

The full code is hosted in the Kelvin App Samples repository on GitHub here.

Repository contents

  • README.md: contains a short description.
  • main.py: the application entrypoint and core logic.
  • requirements.txt: Python dependencies.
  • Dockerfile: used to build a Docker image for deployment.
  • .dockerignore: used to exclude files from Docker builds.
  • app.yaml: application manifest/config for deploying to a Kelvin Instance.
  • model/ (directory): Contains the COmputer Vision model used for categorizing the images.

Basic Code Explanation

The solution consists of two main components:

The following diagram illustrates the architecture of the solution:

  1. Camera Connector ( ../../importers/camera-connector ): Simulates image capture from a camera and publishes the image in base64 format to the Kelvin Platform. In production environments, it would interface directly with live camera feeds to acquire real-time images.
  2. Casting Defect Detection: Processes the acquired images to identify casting defects using a pre-trained TensorFlow machine learning model. It evaluates the images for any anomalies and reports the findings back to the Kelvin Platform for further analysis.