Search Results for author:
Found 7 papers, 4 papers with code
PennSyn2Real: Training Object Recognition Models without Human Labeling
Scalable training data generation is a critical problem in deep learning.
PST900: RGB-Thermal Calibration, Dataset and Segmentation Network
In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques.
Ranked #14 on
Thermal Image Segmentation
on PST900
Mine Tunnel Exploration using Multiple Quadrupedal Robots
Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility.
Robotics
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks
Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection.
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures.
