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

2.3 Data Annotation

In object detection, data annotation includes defining abounding box around the objects in the pictures and associate a class label for each bounding box. The dataset in this demo is collected in a way which simplifies the annotation process. The pictures are taken with only one object in each one. The objects in the pictures in each class are located at the exact position and orientation. For this reason, the bounding box and class label on one picture can be copied to the rest of the pictures in the same class. In such case, only four pictures (one of each class) are annotated and their annotation are copied to the rest of the pictures in their respective classes. The coco annotation standard is followed in this demo.