The goal of this course is to gain hands-on experience with machine learning and computer vision applications on an embedded platform.
Course Content
This course contains; Introduction to NVidia Jetson Nano platform,Basics of Linux operating system,Installing IDE and building face detection application using OpenCV library,Installing CSI camera and implementing computer vision applications on live video,Utilizing GPU functions of OpenCV,Sparse optical flow, mouse click events for user input, and object tracking,Object detection with OpenCV DNN module,TensorRT models,Using TensorRT object detection models and comparing different models ,MediaPipe models ,Using Tesseract for optical character recognition,Project work.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Teaching Methods:
Assessment Methods:
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to NVidia Jetson Nano platform
2
Basics of Linux operating system
3
Installing IDE and building face detection application using OpenCV library
4
Installing CSI camera and implementing computer vision applications on live video
5
Utilizing GPU functions of OpenCV
6
Sparse optical flow, mouse click events for user input, and object tracking
7
Object detection with OpenCV DNN module
8
TensorRT models
9
Using TensorRT object detection models and comparing different models
10
MediaPipe models
11
Using Tesseract for optical character recognition
12
Project work
Resources
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
5
An ability to design and conduct experiments, as well as to analyze and interpret data
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
30
Rate of Final Exam to Success
70
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
0
0
0
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
0
0
0
Term Project
0
0
0
Presentation of Project / Seminar
0
0
0
Quiz
0
0
0
Midterm Exam
0
0
0
General Exam
0
0
0
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
0
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30)
0
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
IMAGE PROCESSING in EMBEDDED SYSTEMS
-
Spring Semester
3+0
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Name of Lecturer(s)
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Assistant(s)
Aim
The goal of this course is to gain hands-on experience with machine learning and computer vision applications on an embedded platform.
Course Content
This course contains; Introduction to NVidia Jetson Nano platform,Basics of Linux operating system,Installing IDE and building face detection application using OpenCV library,Installing CSI camera and implementing computer vision applications on live video,Utilizing GPU functions of OpenCV,Sparse optical flow, mouse click events for user input, and object tracking,Object detection with OpenCV DNN module,TensorRT models,Using TensorRT object detection models and comparing different models ,MediaPipe models ,Using Tesseract for optical character recognition,Project work.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Teaching Methods:
Assessment Methods:
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to NVidia Jetson Nano platform
2
Basics of Linux operating system
3
Installing IDE and building face detection application using OpenCV library
4
Installing CSI camera and implementing computer vision applications on live video
5
Utilizing GPU functions of OpenCV
6
Sparse optical flow, mouse click events for user input, and object tracking
7
Object detection with OpenCV DNN module
8
TensorRT models
9
Using TensorRT object detection models and comparing different models
10
MediaPipe models
11
Using Tesseract for optical character recognition
12
Project work
Resources
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
5
An ability to design and conduct experiments, as well as to analyze and interpret data
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
