Machine-Learning-Based Cellular Drone Detection Capability
Talk on Cellular Drone Detection
99 - GENERAL AND MISCELLANEOUS↗
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White Paper: Drone Detection with Radar
INL recently summarized how Ukraine conducted an unprecedented drone attack on Russian air bases on Sunday, June 1, 2025 using Russian cellular networks to operate the drones. It also showed how a drone with a cellular connection can be detected using network signatures. This report adds an additional section, section 2, on detecting drones with radar if they are not connected to a cellular network and hence network signatures are not available.
42 ENGINEERING↗
Obstacle Detection for Drones Using Machine Learning
Using machine learning, drones are able to detect obstacles in real time utilizing only a camera. Obstacle detection is done with a depth estimation model. The model produces an estimate of the distance of all the objects within the drones line of sight. From this estimate we can then detect if we are close to an obstacle. The method has been applied to a variety of real world videos and achieves 92% accuracy.
47 OTHER INSTRUMENTATION↗
UAS Live Incursion Drills Survey
Unmanned aircraft systems (UAS/drones) are rapidly evolving and are considered an emerging threat by nuclear facilities throughout the world. Due to the wide range of UAS capabilities, members of the workforce and security/response force personnel need to be prepared for a variety of drone incursion situations. Tabletop exercises are helpful, but actual live exercises are often needed to evaluate the quick chain of events that might ensue during a real drone fly-in and the essential kinds of information that will help identify the type of drone and pilot. Even with drone detection equipment, the type of UAS used for incursion drills can have a major impact on detection altitude and finding the UAS in the sky. Using a variety of UAS, the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Security (INS) would like to offer partners the capability of adding actual UAS into workforce and response exercises to improve overall UAS awareness as well as the procedures that capture critical steps in dealing with intruding drones.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Data Exploration of UAV Flights Recorded on Smartphones
The increasing accessibility and technological advancements of unmanned aerial vehicles (UAVs) pose legitimate concerns to nuclear non-proliferation efforts. To deter and combat unwanted or nefarious UAV activity near or around nuclear sensitive sites, the ability to quickly and easily detect drones becomes vital. Our team is exploring the use of ubiquitous smartphones as multi-modal sensors (microphone, accelerometer, gyroscope, etc.) to meet this challenge. In conjunction with Idaho National Lab, two UAV flight tests and one stationary in-air test were conducted and recorded with eight Samsung Galaxy S8 smartphones. This work presents initial characterization and annotation of acoustic signals associated with UAV activity.
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
The Consortium for Advanced Sorghum Phenomics (CASP). Final report
The goal of CASP was to accelerate breeding of biomass sorghum [Sorghum bicolor (L.) Moench] by identifying genotypes exhibiting high yield under well-watered, pre- or post-drought and/or salinity-stress conditions. We did this by combining high-throughput, non-invasive drone phenotyping with genomics and molecular profiling. Field-based phenotyping utilized a multi-modal sensor suite of LiDAR, multispectral cameras, and thermal cameras mounted on a commercial drone to detect traits required for yield prediction and selection of drought and saline tolerant lines of sorghum. Traits of interest included plant height (PH), leaf area index (LAI), wet biomass (BMW), and biomass at 65% moisture (BM65) and were measured from emergence to harvest on a weekly basis over three growing seasons. The final output were measurements of traits on a plot-by-plot basis, identified by the plot ID used by the Proprietary data processing software enabled raw field data to be turned into plant traits and delivered to the PNNL and JGI within the same workday.
09 BIOMASS FUELS↗
Unmanned Aircraft Systems (UAS) and Light Detection and Ranging (LiDAR)/Camera Technologies to Detect Avian Events and Other Environmental Measures at Utility- Scale Power Plants (Final Report)
The goal of this project was to develop and validate two complementary, cost-effective remote sensing technologies to monitor avian fatalities at utility-scale solar facilities: fixed platform (Animal Activity Monitoring-AAM) and aerial-based (Uncrewed Aircraft Systems-UAS). This project used these features with machine learning to automate the detection of avian carcasses and nests at solar facilities.
14 SOLAR ENERGY↗
Aerial 3D Building Reconstruction from Drone Imagery (A3DBR) v1
This toolkit is composed of several modules for extracting buildings geometrical and thermal characteristics from RGB and thermal imagery captured using a drone. - Building 3D reconstruction module: leverage a photogrammetry software to construct a 3D point cloud from RGB drone imagery, which is then used in conjunction with image processing and geometric methods to extract building footprint and building height (i.e., 3D model of the building). - Windows to wall ratio estimation module: leverage deep learning semantic segmentation modeling to detect windows on 2D drone RGB images. The detected windows are then projected onto the extracted building 3D model (using building 3D reconstruction module) and their area is computed to obtain window to wall ratio estimation. - Thermal anomalies detection module: leverage image processing and machine learning algorithm to detect on 2D drone thermal images potential thermal anomalies within building's facades and roofs.
Granderson, Jessica↗
High Speed and Low-Noise Open-Path Gas Detection System
Explore the source record for details and available documents.
07 ISOTOPE AND RADIATION SOURCES↗
Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares
This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.
47 OTHER INSTRUMENTATION↗
Multiplexed Toxin Activity Detection
There is a need for field forward biothreat detection methods for highly lethal marine toxins, such as conotoxins, to protect the warfighter and civilians in contested environments. These toxins arise from harmful algal blooms which impact the warfighter, pets, and civilians. The need for detection platforms that can be fielded on drones or unmanned vehicles is paramount to successful biothreat detection to serve as an early warning system for those impacted in these environments.
59 BASIC BIOLOGICAL SCIENCES↗
Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems
In a nuclear accident, or in the aftermath of an improvised nuclear device or radiological dispersal device detonation, radioactive materials may pose a severe health threat to individuals near the site of the incident. The Intelligent Consequence Control by Aerial Reconnoiter Using Unmanned Systems (ICARUS) does the dull, dirty, and dangerous (and, sometimes, deep) work of mapping and characterizing valuable information to enable a safe and appropriate response. ICARUS provides timely answers to the questions, “What is it?” “Where is it?”, and “How far does it extend?” Using commercial, off-the-shelf proven components, the integrated chemical, biological, radiological, nuclear, and explosives detection architecture for our drones provides a solution for collecting radiological, chemical, and optical information on developing complex situations, returning actionable data for determining if it is safe for further response efforts. ICARUS can determine and transmit the location, identity, and intensity of radionuclide contamination to a remote base station. It can deliver on the unexpected.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Radiological Scouting, Monitoring and Inspection Using Drones
Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices (“dirty bombs”). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger–Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium–Zinc–Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.
Pinto, Luís Ramos (ORCID:0000000319975484)↗
Fast Data Processing for Hyperspectral Sensors on Small Platforms
Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Detecting gas leaks using unmanned aerial vehicles
Methods, systems and computer program products for detecting gas leaks using a drone are provided. Aspects include capturing a first set of data regarding a presence of a gas in the geographic area while flying along the initial flight path. Aspects also include creating secondary flight paths through regions in the geographic area in which the presence of the gas exceeds a threshold amount and capturing a second set of data regarding a concentration of the gas in the one or more regions while flying along the secondary flight paths. Aspects further include capturing wind data while flying along the initial and second flight paths and creating a three-dimensional gas plume model for gas leaks identified in the geographic area based on the first set of data, the second set of data and the wind data, wherein the three-dimensional gas plume model identifies a source of the gas leaks.
47 OTHER INSTRUMENTATION↗
Polarimetry-Enhanced Imaging towards Autonomous Solar Field and Receiver Inspections
During the typical operation of a Concentrating Solar Power (CSP) plant, a large portion of the energy (~45%) can be lost due to various imperfect conditions, such as blocking, shading, mirror soiling, tracking and canting errors, etc. It is necessary to develop efficient and effective field inspection technology to optically survey and characterize a CSP field, which can be used as an input for autonomous control, maintenance scheduling and spot repair whenever necessary to maximize the overall efficiency of the plant. In this project, We aim to apply polarimetric imaging for CSP collector and receiver inspection and develop polarimetric drone cameras (via integrating polarimetric imagers onto drones) for autonomous field inspection in CSP plants.
14 SOLAR ENERGY↗
High-Resolution Sampling of a River Plume Front with Uncrewed Underwater and Aerial Vehicles
Sampling fast-propagating oceanic features is inherently challenging and demands versatile instrumentation and innovative strategies. This paper introduces a novel sampling strategy designed to capture such phenomena, exemplified by a river plume front. Our method revolves around modifying the preprogrammed pathway of an uncrewed underwater vehicle (UUV) to dynamically track and three-dimensionally sample the evolution of the front. To enable the UUV to follow the feature, we adapt the use of a drifting gateway buoy to be positioned and trapped at the front’s convergence zone, allowing underway navigation relative to the buoy. In our demonstration, we showcase the effectiveness of this strategy by successfully conducting over 30 crossings of a river plume front within a 6-h window. The UUV sensors allowed a comprehensive assessment of key front characteristics, including density, velocity, and turbulence. Supplemental drone footage contributed to the overall picture and facilitated the transformation of the dataset into a front-following reference frame. This article provides an in-depth description of the deployment strategy and required postcollection data processing, including frontal crossing detection, the assessment of the frontal orientation from drone footage, and defining the plume bottom boundaries using backscatter intensity contours.
autonomous observations↗
Vegetation Detection Using Deep Learning and Conventional Methods
Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.
58 GEOSCIENCES↗