10 papers
Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees
Padam Jung Thapa, Anav Katwal, Ayon Dey +4
Sand boils, points where water seeping beneath an earthen levee re-emerges at the surface, are early warnings of internal erosion, and deep segmentation networks are increasingly u…
EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion
Johny J. Lopez, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4
Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing methods achieve high accuracy…
VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification
Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi +4
Deploying deep learning models on embedded devices for tasks such as aerial disaster monitoring and infrastructure inspection requires architectures that balance accuracy with stri…
KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +3
Semantic segmentation of structural defects in civil infrastructure remains challenging due to variable defect appearances, harsh imaging conditions, and significant class imbalanc…
Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation
Christina Thrainer, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4
Few-shot semantic segmentation is vital for deep learning-based infrastructure inspection applications, where labeled training examples are scarce and expensive. Although existing…
Few-Shot Learning in Video and 3D Object Detection: A Survey
Md Meftahul Ferdaus, Kendall N. Niles, Joe Tom +2
Few-shot learning (FSL) enables object detection models to recognize novel classes given only a few annotated examples, thereby reducing expensive manual data labeling. This survey…