This course will introduce object detection approach based on deep learning, which is suitable autonomous driving applications. The topics will cover two stage approaches, single stage approaches and discuss how to accelerate the execution of these deep learning models to meet real time constraints and improve their accuracy. Finally, a real case will be studied to show their strength and weakness.
After taking this course, students will learn the principle and limitations of the object detection based on deep learning, and know their real time constraints and accuracy problems and their solutions.
Tian-Sheuan Chang received the B.S., M.S., and Ph.D. degrees in electronic engineering from National Chiao-Tung University (NCTU), Hsinchu, Taiwan, in 1993, 1995, and 1999, respectively.
From 2000 to 2004, he was a Deputy Manager with Global Unichip Corporation, Hsinchu, Taiwan. In 2004, he joined the Department of Electronics Engineering, NCTU, where he is currently a Professor. In 2009, he was a visiting scholar in IMEC, Belgium. His current research interests include system-on-a-chip design, VLSI signal processing, and computer architecture.
Dr. Chang has received the Excellent Young Electrical Engineer from Chinese Institute of Electrical Engineering in 2007, and the Outstanding Young Scholar from Taiwan IC Design Society in 2010. He has been actively involved in many international conferences as an organizing committee or technical program committee member.
第1週:Two stage detection 兩段式物件偵測
第2週:Single stage object detection 一段式物件偵測
第3週:Fast object detection and small object detection 快速物件偵測與小物件偵測
第4週:Improvement and real case study 效果增進與實例探討
Two stage detection
1-1 Object Detection
1-3 Fast R-CNN
1-4 Faster R-CNN
Single stage object detection
1-6 Single Stage Object Detection
1-7 SSD: Single Shot MultiBox Detector
1-8 YOLO v2 v3
Fast object detection and small object detection
1-10-2 Object Detection at 200FPS
1-12 Mask R-CNN
1-13 Small Object Detection
Improvement and real case study
1-14-1 Improvement Over Mask R-CNN and RetinaNet-1
1-14-2 Improvement Over Mask R-CNN and RetinaNet-2
1-15 Uber Event
本課程分為 15 個單元，每週配合課程內容提供隨堂測驗，以幫助學習者快速確認是否瞭解上課內容，另安排各單元測驗用以考核學習成果，考核標準請參見「評分標準說明」。