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Blekinge Institute of Technology
Department of Technology and Aesthetics
Revision: 2
Reg.no: BTH-4.1.14-0436-2024
Course syllabus
Introduction to hybrid radar and camera system
Introduction to hybrid radar and camera system
6 credits (6 högskolepoäng)
Course code: DV2655
Main field of study: Computer Science
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
Language of instruction: English
Applies from: 2024-04-23
Approved: 2024-04-23
1. Descision
This course is established by Dean 2023-12-23. The course syllabus is approved by Head of Department of Technology and Aesthetics 2024-04-23 and applies from 2024-04-23.
2. Entry requirements
Admission to the course requires 60 completed credits in Computer Science of which 6 credits are in programming.
3. Objective and content
3.1 Objective
The course covers basic and advanced sensor fusion deep learning methods that can be used for a variety of tasks in hybrid radar and camera systems. In addition to the theoretical content, students will familiarize themselves with different features of deep learning tools to design, train and debug neural networks in practice on real hybrid radar and camera systems.
3.2 Content
- Basic image acquisition with a camera
- Basic image processing and filtering
- Basic radar techniques
- Fundamentals of deep neural networks for regression problems
- Fundamentals of FMCW radar
- Sensor fusion with radar and camera
- Data association and sensor synchronization
- Sensor fusion object detection and tracking
- Implementing object detection from hybrid camera-radar systems
4. Learning outcomes
The following learning outcomes are examined in the course:
4.1. Knowledge and understanding
On completion of the course, the student will be able to:
- to describe and understand the process of camera imaging,
- to describe and understand the process of radar scanning,
- to describe and understand the regression problem,
- to understand basic ideas on how deep neural networks can be used for the regression problem,
- to describe and recognize the problem of hybrid systems,
- to describe and understand where and how sensor fusion can be used.
4.2. Competence and skills
On completion of the course, the student will be able to:
- applying object detection with sensors such as camera and radar,
- implementing object detection in hybrid camera-radar systems,
- implement object tracking and motion analysis in hybrid camera-radar systems,
- use and implement deep neural network applications for hybrid camera-radar systems,
- Implement the basic algorithms with appropriate modifications for the available data, primarily fusion data from hybrid sensors.
4.3. Judgement and approach
On completion of the course, the student will be able to:
- independently and critically evaluate and analyze their solutions,
- Write a technical report that describes, in a concise technical prose, the work done to object detection and tracking, analyze and validate in a related hybrid system problem.
5. Learning activities
The course is given as a campus course. Teaching consists of lectures, exercises, laboratory sessions and projects.
The lectures present theories and contribute to the theoretical understanding required to complete the course. Exercises, laboratory sessions and projects apply the theory in practical sessions. Projects and laboratory sessions are carried out in groups.
The course assumes that the student has, or acquires, the ability to independently seek information to solve problems and difficulties that arise.
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2410 | On-campus Examination | 2.0 credits | AF |
| 2420 | Laboratory Session | 1.0 credits | GU |
| 2430 | Project | 3.0 credits | AF |
The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.
The information before a course occasion states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.
An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.
7. Course evaluation
The course evaluation should be carried out in line with BTH:s course evaluation template and process.
8. Restrictions regarding degree
The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.
9. Course literature and other materials of instruction
Course material from the department
This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.