13 citations · 29 across the 22 of their papers we have counts for
16 papers · 1 filter
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…
DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation
Enrique Hernandez Noguera, Md Meftahul Ferdaus, Elias Ioup +1
Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imbalance, and the need for prec…
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…
Edge-Optimized Vision-Language Models for Underground Infrastructure Assessment
Johny J. Lopez, Md Meftahul Ferdaus, Mahdi Abdelguerfi
Autonomous inspection of underground infrastructure, such as sewer and culvert systems, is critical to public safety and urban sustainability. Although robotic platforms equipped w…
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…
Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
The rising incidence of natural and human-induced disasters necessitates robust visual recognition systems capable of operating under limited labeled data conditions. However, disa…
ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Few-Shot Learning (FSL), which involves learning to generalize using only a few data samples, has demonstrated promising and superior performances to ordinary CNN methods. While Ba…
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…
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…
FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks
Christina Thrainer, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4
Automated structural defect segmentation in civil infrastructure faces a critical challenge: achieving high accuracy while maintaining computational efficiency for real-time deploy…
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +4
We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmog…
DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed sig…
Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network
Rasha Alshawi, Md Meftahul Ferdaus, Mahdi Abdelguerfi +3
Imbalanced datasets are a significant challenge in real-world scenarios. They lead to models that underperform on underrepresented classes, which is a critical issue in infrastruct…
SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes
Rasha Alshawi, Md Meftahul Ferdaus, Md Tamjidul Hoque +4
This paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net integrates a bottom-up pathway featuring…
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches
Gao Yu Lee, Jinkuan Chen, Tanmoy Dam +3
High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Sin…
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
Rasha Alshawi, Md Tamjidul Hoque, Md Meftahul Ferdaus +7
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets wi…