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

4.3 Dashboard and Bounding Boxes Drawing

The dashboard graphically shows an overview of the performance of the whole manufacturing system including the total number of units, the percentage of the defected units, and the rate of production in units per hour. It also shows a histogram of the types of defects. Such information is useful to analyze the manufacturing system and select the most common types of defects. The dashboard code is contained in its own class which is saved in the dashboard.py file. A new class is added to the post_process.py to control all post process work related to the defect detection demo including calling the object tracker, performance statistics calculation, calling dashboard generator, and draw bounding boxes.