130 citations · 206 across the 10 of their papers we have counts for
11 papers
SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift
Sagar Lekhak, Prasanna Reddy Pulakurthi, Lalit Joshi +2
RGB imagery offers a practical, low-cost option for Unmanned Aerial/Ground Vehicle (UAV/UGV) survey support in surface-landmine detection, but object detectors remain underexplored…
Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection
Sagar Lekhak, Prasanna Reddy Pulakurthi, Emmett J. Ientilucci
Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found.…
Improved Knowledge Distillation for Land-Use Image Classification
Arundhuti Sur, Abhiroop Chatterjee, Susmita Ghosh +1
In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image class…
Benchmarking Deep Learning and Statistical Target Detection Methods for PFM-1 Landmine Detection in UAV Hyperspectral Imagery
Sagar Lekhak, Prasanna Reddy Pulakurthi, Ramesh Bhatta +1
In recent years, unmanned aerial vehicles (UAVs) equipped with imaging sensors and automated processing algorithms have emerged as a promising tool to accelerate large-area surveys…
Uncertainty Quantification In Surface Landmines and UXO Classification Using MC Dropout
Sagar Lekhak, Emmett J. Ientilucci, Dimah Dera +1
Detecting surface landmines and unexploded ordnances (UXOs) using deep learning has shown promise in humanitarian demining. However, deterministic neural networks can be vulnerable…
A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Sagar Lekhak, Emmett J. Ientilucci, Jasper Baur +1
This paper introduces a novel benchmark dataset of Visible and Near-Infrared (VNIR) hyperspectral imagery acquired via an unmanned aerial vehicle (UAV) platform for landmine and un…
Adaptive Contextual Embedding for Robust Far-View Borehole Detection
Xuesong Liu, Tianyu Hao, Emmett J. Ientilucci
In controlled blasting operations, accurately detecting densely distributed tiny boreholes from far-view imagery is critical for operational safety and efficiency. However, existin…
SmokeNet: Efficient Smoke Segmentation Leveraging Multiscale Convolutions and Multiview Attention Mechanisms
Xuesong Liu, Emmett J. Ientilucci
Efficient segmentation of smoke plumes is crucial for environmental monitoring and industrial safety, enabling the detection and mitigation of harmful emissions from activities lik…
AeroRIT: A New Scene for Hyperspectral Image Analysis
Aneesh Rangnekar, Nilay Mokashi, Emmett Ientilucci +2
We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that…
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
Aneesh Rangnekar, Nilay Mokashi, Emmett Ientilucci +2
Inferring spectral signatures from ground based natural images has acquired a lot of interest in applied deep learning. In contrast to the spectra of ground based images, aerial sp…
A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds
Mohammed Yousefhussien, David J. Kelbe, Emmett J. Ientilucci +1
When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally…