DRT 492 · 3(2-3-5) · Year/term 4/1

Computer Vision Applications

การประยุกต์ใช้การมองเห็นด้วยคอมพิวเตอร์

Progress

Notional hours: 150 h (online/self-study 75 · in class/lab 75)

Course description

Computer vision tasks in UAS; image processing with OpenCV; detection and segmentation; geospatial AI; developing image-processing applications; evaluation and deployment.

Thai description

งานการมองเห็นด้วยคอมพิวเตอร์ในอากาศยานไร้คนขับ การประมวลผลภาพด้วย OpenCV การตรวจจับและแบ่งส่วนภาพ ปัญญาประดิษฐ์เชิงภูมิสารสนเทศ การพัฒนาแอปพลิเคชันประมวลผลภาพ การประเมินและนำระบบไปใช้งาน

Description source: New draft for the curriculum committee

Notes for the curriculum committee

ทับซ้อนกับ DRT 337 ควรกำหนดให้ DRT 492 เน้นการพัฒนาแอปพลิเคชันเชิงประยุกต์

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Select computer vision techniques for problems	PLO4
CLO2	Develop drone image-processing applications	PLO4PLO5
CLO3	Evaluate and improve application performance	PLO4

Learning modules

1CV tasks for drones
Weeks 1–3 · 30 h1 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

Computer vision and the path to edge AITH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

2OpenCV
Weeks 4–6 · 30 h1 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

OpenCVTH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

3Detection and segmentation
Weeks 7–9 · 30 h2 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

4GeoAI
Weeks 10–12 · 30 h1 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

GeoAI and publishing mapsTH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

5Evaluation and deployment
Weeks 13–15 · 30 h2 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

Assessment (draft)

Labs and worksheets	35%
Module quizzes	10%
Midterm examination	20%
Mini-project or practical exam	35%

Knowledge domain

D11 Artificial intelligence and computer vision D13 Surveying, mapping and geoinformatics

Key references

Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer. link
OpenCV. OpenCV documentation. link