SPRADC9 july   2023 AM62A3 , AM62A7

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1Introduction
    1. 1.1 Defect Detection Demo Summary
    2. 1.2 AM62A Processor
    3. 1.3 Defect Detection Systems
    4. 1.4 Conventional Machine Vision vs Deep Learning
  5. 2Data Set Preparation
    1. 2.1 Test Samples
    2. 2.2 Data Collection
    3. 2.3 Data Annotation
    4. 2.4 Data Augmentation
  6. 3Model Selection and Training
    1. 3.1 Model Selection
    2. 3.2 Model Training and Compilation
  7. 4Application Development
    1. 4.1 System Flow
    2. 4.2 Object Tracker
    3. 4.3 Dashboard and Bounding Boxes Drawing
    4. 4.4 Physical Demo Setup
  8. 5Performance Analysis
    1. 5.1 System Accuracy
    2. 5.2 Frame Rate
    3. 5.3 Cores Utilization
    4. 5.4 Power Consumption
  9. 6Summary
  10. 7References

1.1 Defect Detection Demo Summary

Defect detection is a crucial part of quality assurance in the manufacturing process. This demo uses AM62A to run a vision based artificial intelligence model for defect detection for manufacturing applications. The model tests the produced units as they move on a conveyor belt, to recognize the accepted and the defected units. Figure 1-1 shows a screenshot of the application.

GUID-20230630-SS0I-ZGFF-ZCCV-59V4XZS2X6W4-low.png Figure 1-1 Screenshot of the Defect Detection End Application Using AM62A (Right side is a live video feed with color boxes marking the detected objects. Left side is a graphical quality control dashboard.)

An object tracker is developed for this demo to provide accurate coordinates of the units for sorting and filtering. A live video is displayed on the screen. The units are marked on the screen using green boxes for good (accepted) units while defected units are marked with boxes with different shades of red to distinguish the types of defects. The screen also includes a graphical dashboard showing live statistics about total products, defect percentage, production rate, and a histogram of the types of defect. The object tracker and the graphical dashboard are built using Python. The code base and details of how to run the demo are available on TI marketplace as a Github repo: https://github.com/TexasInstruments/edgeai-gst-apps-defect-detection.

The steps followed to develop this application includes:

  • Data collection and preparation
  • Model selection, training, and compilation
  • Model evaluation and deployment
  • Application code development including object tracker and graphical dashboard
  • System performance analysis and power consumption estimation