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
Session:
TH2.P9: UAV-based Multi-sensor Identification and Mapping of Surface and Buried Explosive Ordnance II Oral
Track:
Community-Contributed Sessions
Location:
Plaza: Room P9
Presentation Time:
Thursday, 7 August, 11:00 - 11:15
Session Co-Chairs:
Basani Lammy Nkuna, Agricultural Research Council and Emmett J. Ientilucci, Rochester Institute of Technology
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 SURFACE 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
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: Identifying Diseased Maize Leaves in Mopani District, Limpopo: A UAV Multispectral Imaging Approach Using Vegetation Indices and Machine Learning
Basani Lammy Nkuna, Wonga Masiza, Johannes George Chirima, Solomon W. Newete, Agricultural Research Council, South Africa; Adriaan Johannes Van Der Walt, University of the Free State, South Africa; Adolph Nyamugama, , Agricultural Research Council, South Africa
Resources