TH1.R1: Advanced Learning Models for Object Detection
Thu, 20 Jul, 08:30 - 09:45 Pacific Time (UTC -7)
Location: Room 1
Session Type: Oral
Track: Data Analysis
Thu, 20 Jul, 08:30 - 08:42 Pacific Time (UTC -7)

TH1.R1.1: MACHINE LEARNING-BASED IMAGE DETECTION OF DEEP-SEA SEAMOUNTS CREATURES
Aiyue Liu, Xiaofeng Li, Chinese Academy of Science, China
Thu, 20 Jul, 08:42 - 08:54 Pacific Time (UTC -7)

TH1.R1.2: Strip-Convolution based U-Net for Crack Detection
Jun Luo, Wuhan University, China; Min Wei, Yi Xu, Xin Su, Zheng Zhou, Changjiang Survey Planning Design and Research Co., Ltd., China
Thu, 20 Jul, 08:54 - 09:06 Pacific Time (UTC -7)

TH1.R1.3: RGB-INFRARED MULTI-MODAL REMOTE SENSING OBJECT DETECTION USING CNN AND TRANSFORMER BASED FEATURE FUSION
Tao Tian, Yang Xu, Zebin Wu, Zhihui Wei, Nanjing University of Science and Technology, China; Jiang Cai, Nanjing Research institute of electronics engineering (NRIEE), China; Jocelyn Chanussot, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, France
Thu, 20 Jul, 09:06 - 09:18 Pacific Time (UTC -7)

TH1.R1.4: THE INFLUENCE OF INPUT IMAGE SCALE ON DEEP LEARNING-BASED BELUGA WHALE DETECTION FROM AERIAL REMOTE SENSING IMAGERY
Muhammed Patel, Xinwei Chen, Linlin Xu, Fernando Pena Cantu, Javier Noa Turnes, Neil Brubacher, David Clausi, Andrea Scott, University of Waterloo, Canada
Thu, 20 Jul, 09:18 - 09:30 Pacific Time (UTC -7)

TH1.R1.5: CDQN: Context infused Sequential Object Detection with Deep Reinforcement Learning in Aerial Images
Rohit Gandikota, Northeastern University, United States; Deepak Mishra, Indian Insitute of Space Science and Technology, India
Thu, 20 Jul, 09:30 - 09:45 Pacific Time (UTC -7)

TH1.R1.D: Discussion