TH1.R1.3

MACHINE LEARNING-BASED IMAGE DETECTION OF DEEP-SEA SEAMOUNTS CREATURES

Aiyue Liu, Xiaofeng Li, Chinese Academy of Science, China
Session:
TH1.R1: Advanced Learning Models for Object Detection Oral
Track:
Data Analysis
Location:
Room B
Presentation Time:
Thu, 20 Jul, 08:54 - 09:06 Pacific Time (UTC -7)
Session Co-Chairs:
Aiyue Liu, Chinese Academy of Science and Xinwei Chen, University of Waterloo
Session Manager:
Abhishek Singh
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Session TH1.R1
TH1.R1.1: RGB-INFRARED MULTI-MODAL REMOTE SENSING OBJECT DETECTION USING CNN AND TRANSFORMER BASED FEATURE FUSION
Tao Tian, Nanjing University of Science and Technology, China; Jiang Cai, Nanjing Research institute of electronics engineering (NRIEE), China; Yang Xu, Zebin Wu, Zhihui Wei, Nanjing University of Science and Technology, China; Jocelyn Chanussot, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, France
TH1.R1.2: 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
TH1.R1.3: MACHINE LEARNING-BASED IMAGE DETECTION OF DEEP-SEA SEAMOUNTS CREATURES
Aiyue Liu, Xiaofeng Li, Chinese Academy of Science, China
TH1.R1.4: 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
TH1.R1.5: A HYBRID METHOD FOR VESSEL DETECTION IN SATELLITE IMAGES
Ioannis Karantaidis, Konstantina Bereta, Dimitris Zissis, MarineTraffic, Greece
Resources
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