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

Abstract

AM62A system-on-chip (SoC) is used to build an end-end application for defect detection in manufacturing. AM62A is a heterogeneous processor equipped with a 2 TOPS Deep Learning Accelerator and up to four Arm® Cortex® A53 processors in addition to various other accelerators for Video and Vision processing. The various compute cores and rich peripheral set make AM62A an ideal option for applications where advanced sensor processing capability is required in real-time. This document describes the complete process of building a defect detection application starting from data collection, deep learning model selection, model training and model deployment. It shows how TI’s EdgeAI Studio tools simplify this process. System level performance analysis of the application, resource utilization and power profiling using TI's tools are presented. Source code and a step-by step guide in TI’s github repository are also available and links are provided for the interested developer at: https://github.com/TexasInstruments/edgeai-gst-apps-defect-detection.