本课程介绍以深度学习为基础的对象侦测作法，适用于自动驾驶或辅助驾驶设计。将会介绍各种经典的两段式与一段式作法，并探讨如何加速符合即时应用，与如何增加准确率的各种做法，最后将以实例说明实际应用场景所遇到的问题与效果。
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.
預期學生修完本門課，能深入了解以深度學習為基礎的物件偵測背後的原理與其限制，並對實際應用所面臨的即時運算與準確率問題，知道如何解決。
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.
張添烜博士於2000年於交大電子獲得博士學位，於2000~2004任職於創意電子擔任副理，於2004年加入交大電子擔任教職至今。於2009年至比利時IMEC擔任訪問學者。張博士曾獲中國電機工程學會優秀年輕電機工程師獎，台灣IC設計學會傑出年輕學者獎。他的專長為VLSI 設計，深度學習與訊號處理。
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周:Two stage detection 两段式对象侦测
第2周:Single stage object detection 一段式对象侦测
第3周:Fast object detection and small object detection 快速对象侦测与小对象侦测
第4周:Improvement and real case study 效果增进与实例探讨
周次
单元主题
第一周
Two stage detection
两段式对象侦测
1-1 Object Detection
1-2 R-CNN
1-3 Fast R-CNN
1-4 Faster R-CNN
1-5 R-FCN
第二周
Single stage object detection
一段式对象侦测
1-6 Single Stage Object Detection
1-7 SSD: Single Shot MultiBox Detector
1-8 YOLO v2 v3
1-9 RetinaNet
第三周
Fast object detection and small object detection
快速对象侦测与小对象侦测
1-10-1 PVANet
1-10-2 Object Detection at 200FPS
1-11 Comparison
1-12 Mask R-CNN
1-13 Small Object Detection
第四周
Improvement and real case study
效果增进与实例探讨
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」。
本課程建議具備基本深度學習概念即可，無須太多背景知識，適合所有對深度學習於物件偵測有興趣的學習者修習。
本課程證書費用：500元
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