ไทยTH
UAT 470 · 3(2-2-5) · Year/term 4/1

Intelligent Image Analysis Systems

ระบบวิเคราะห์ภาพอัจฉริยะ

Progress
Notional hours: 135 h (online/self-study 75 · in class/lab 60)

Course description

image-data preparation, image processing, feature extraction, image classification, object detection and tracking, image segmentation, anomaly detection, deep-learning models, and evaluation of automated image-analysis systems

Thai description

การเตรียมข้อมูลภาพ การประมวลผลภาพ การสกัดคุณลักษณะ การจำแนกภาพ การตรวจจับและติดตามวัตถุ การแบ่งส่วนภาพ การตรวจหาความผิดปกติ การใช้แบบจำลองการเรียนรู้เชิงลึก และการประเมินระบบวิเคราะห์ภาพอัตโนมัติ

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 465

Course learning outcomes (CLO)

CLOOutcomePLO
CLO1Explain intelligent image analysis pipelinesPLO4
CLO2Develop detection, tracking and counting systemsPLO4
CLO3Evaluate accuracy, latency and fairnessPLO4PLO6

Learning modules

1Video analytics
Weeks 1–3 · 27 h2 KU

Online (before class) · 15 h

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

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
2Datasets and labelling
Weeks 4–6 · 27 h2 KU

Online (before class) · 15 h

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

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
3Detection and tracking
Weeks 7–9 · 27 h2 KU

Online (before class) · 15 h

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

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
4Edge and real time
Weeks 10–12 · 27 h2 KU

Online (before class) · 15 h

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

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
5Evaluation and ethics
Weeks 13–15 · 27 h3 KU

Online (before class) · 15 h

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

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

Assessment (draft)

Labs and worksheets35%
Module quizzes10%
Midterm examination20%
Mini-project or practical exam35%

Knowledge domain

D04 Sensors and embedded systemsD09 Law, safety and riskD11 Artificial intelligence and computer visionD13 Surveying, mapping and geoinformaticsD14 Inspection, industry and surveillanceD18 Management, innovation and professional practice

Key references

  1. Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer. link
  2. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly.
  3. UNESCO. (2021). Recommendation on the ethics of artificial intelligence. link