EMBEDDED ARTIFICIAL INTELLIGENCE and COMPUTER VISION

AIM

Develop artificial intelligence and computer vision applications in edge devices (Nvidia Jetson)

CONTENT

This course contains; Introduction to Linux operating system,Installation of Nvidia Jetson Nano,Face detection application,Installation and use of CSI camera,Utilizing GPU functions of OpenCV,Optical flow and object detection applications,OpenCV DNN module applications,TensorRT model optimization and usage,Mediapipe application,Tesseract application,Nvidia Jetson GPIO usage,Semester project progress (I),Semester project progress (II),Project demo.

1. 1

   Develops artificial intelligence and computer vision applications in resource constraint platforms

   Taught by: Self Study Method · Assessed by: Project Task

2. 2

   Uses Nvidia Jetson platform

   Taught by: Self Study Method · Assessed by: Project Task

WEEKLY PLAN

1. WEEK 1

   Introduction to Linux operating system

2. WEEK 2

   Installation of Nvidia Jetson Nano

3. WEEK 3

   Face detection application

4. WEEK 4

   Installation and use of CSI camera

5. WEEK 5

   Utilizing GPU functions of OpenCV

6. WEEK 6

   Optical flow and object detection applications

7. WEEK 7

   OpenCV DNN module applications

8. WEEK 8

   TensorRT model optimization and usage

9. WEEK 9

   Mediapipe application

10. WEEK 10

    Tesseract application

11. WEEK 11

    Nvidia Jetson GPIO usage

12. WEEK 12

    Semester project progress (I)

13. WEEK 13

    Semester project progress (II)

14. WEEK 14

    Project demo

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

TEACHING STAFF

• Prof.Dr. Bahadır Kürşat GÜNTÜRKCOORDINATOR
• Assist.Prof. Mustafa TÜRKBOYLARI