TH2.P9.3
Sparse-Enhanced Dynamic Fusion Experts Network for Multi-modal Object Detection
zhenyu gao, xin wu, Beijing University of Posts and Telecommunications, China; Jocelyn Chanussot, Univ. Grenoble Alpes, Inria, France
Session:
TH2.P9: UAV-based Multi-sensor Identification and Mapping of Surface and Buried Explosive Ordnance II Oral
Track:
Community-Contributed Sessions
Location:
Room P9
Presentation Time:
Thursday, 7 August, 11:00 - 11:15
Presentation
Discussion
Resources
No resources available.
Session TH2.P9
TH2.P9.1: A CUSTOMIZED NEURAL NETWORK MODEL FOR DETECTING FOREIGN OBJECT DEBRIS IN UAS-BORNE SAR IMAGES
Jingfeng Shan, Lapo Miccinesi, Alessandra Beni, Luca Bigazzi, Massimiliano Pieraccini, University of Florence, Italy
TH2.P9.2: UNCERTAINTY QUANTIFICATION IN LANDMINES AND UXO CLASSIFICATION USING MC DROPOUT
Sagar Lekhak, Emmett J. Ientilucci, Dimah Dera, Rochester Institute of Technology, United States; Susmita Ghosh, Jadavpur University, India
TH2.P9.3: Sparse-Enhanced Dynamic Fusion Experts Network for Multi-modal Object Detection
zhenyu gao, xin wu, Beijing University of Posts and Telecommunications, China; Jocelyn Chanussot, Univ. Grenoble Alpes, Inria, France
TH2.P9.4: MINE DETECTION USING HSI FROM UAV/UGV: A BRIEF OVERVIEW OF POSSIBLE DIRECT AND INDIRECT APPROACHES
Rob Haelterman, Skralan Hosteaux, Charles Hamesse, Royal Military Academy, Belgium
TH2.P9.5: Visually Obstructed Landmine Detection by Fusing UAV-based Magnetometry with Affordable Sensors
Eliza Barnett, Jason Bowman, Nestor Mandujano, Roman Kala Romanowski, Alina Scholz, Will Sedo, Andrew Williams, Simone Yang, Jason Gallicchio, Harvey Mudd College, United States; Christopher Cotner, Ivan Dudiak, Xander Fries, Sorin Jayaweera, Sokil Inc., United States
Resources
No resources available.