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

Data Collection

Four classes are used to train the model: Good (accepted) and three classes of defects including Half Ring, No Plastic, No Ring. Figure 2-2 shows examples of the four classes. The images in the figure are cropped for clarity purposes. The four classes here are selected for demo purposes.

GUID-20230630-SS0I-LPJW-B9PP-RTZKZDTSKWJB-low.jpg Figure 2-2 Examples of Pictures From the Four Classes (the pictures are cropped for clarity purposes)

A custom data collection protocol is followed to simplify the image capturing and annotation. The pictures are taken with top view angel and the camera is positioned at a height that is approximate to the height expected in the actual demo setup. The pictures are captured with a resolution of 720x720. 100 pictures were taken for each class (total 400 pictures). Only one sample is used in the 100 pictures for each class. The samples are positioned at the same orientation while the lighting condition is changed for each picture. This setup helps in the annotation of the pictures where the annotation (bounding box and class label) can be copied between pictures of the same class. In the same time, it provides a comprehensive set of pictures which cover various lighting conditions. While all objects are positioned in one place in the picture, the model can generalize to other areas of the picture. Figure 2-3 shows samples of the pictures captured for the good class.

GUID-20230630-SS0I-M4FT-LB1G-BDL0FPB179TZ-low.jpg Figure 2-3 Samples of Pictures Captured for the Good Class (the pictures are captured at 720x720 resolution)