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DOE OSTI2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

Aarrestad, Thea Klaeboe [ETH, Zurich (main)]↗

DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Mitigation of spatial nonstationarity with vision transformers

Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.

58 GEOSCIENCES↗

DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗

DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Automated Progress Monitoring in Modular Construction Factories Using Computer Vision and Building Information Modeling

Modular construction methods have recently gained interest due to the advantages offered in terms of safety, quality, and productivity for projects. In this method, a significant portion of the construction is performed off-site in factories where modular components are built in different workstations, assembled on the production line, and shipped to the site for installation. Due to the labor-intensive nature of tasks, cycle times in modular construction factories are highly variable, which commonly leads to major bottlenecks and delays in construction projects. To remedy this effect, recent methods rely on sensors such as RFID to monitor the production process, which is reportedly expensive, and intrusive to the work process. Recently, computer vision-based methods have been proposed to track the production process in modular construction factories. However, these methods overlook monitoring the assembly process on the production line. Therefore, this paper presents a method to monitor the assembly process by integrating computer vision-based methods with Building Information Modeling (BIM). The proposed method detects the modular units using object segmentation; superimposes the installation area with the corresponding 2D region using BIM, and identifies the installation of the components using image processing techniques. The proposed method has been validated using surveillance videos captured from a modular construction factory in the US. Successful implementation of the proposed method can lead to timely identification of delays during the assembly process and reduce delays in modular integrated construction projects.

building information modeling↗

DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1997

Low Power Smart Vision System Based on Active Pixel Sensor Integrated with Programmable Neural Processor

A low power smart vision system based on a large format (currently 1Kx1K) active pixel sensor (APS) integrated with a programmable neural processor for fast vision applications is presented.

smart vision system APS programmable neural proces↗

NASA NTRS2015

VISIONS: Remote Observations of a Spatially-Structured Filamentary Source of Energetic Neutral Atoms near the Polar Cap Boundary During an Auroral Substorm

We report initial results from the VISualizing Ion Outflow via Neutral atom imaging during a Substorm (VISIONS) rocket that flew through and near several regions of enhanced auroral activity and also sensed regions of ion outflow both remotely and directly. The observed neutral atom fluxes were largest at the lower energies and generally higher in the auroral zone than in the polar cap. In this paper, we focus on data from the latter half of the VISIONS trajectory when the rocket traversed the polar cap region. During this period, many of the energetic neutral atom spectra show a peak at 100 electronvolts. Spectra with peaks around 100 electronvolts are also observed in the Electrostatic Ion Analyzer (EIA) data consistent with these ions comprising the source population for the energetic neutral atoms. The EIA observations of this low energy population extend only over a few tens of kilometers. Furthermore, the directionality of the arriving energetic neutral atoms is consistent with either this spatially localized source of energetic ions extending from as low as about 300 kilometers up to above 600 kilometers or a larger source of energetic ions to the southwest.

Polar Cap↗

NASA NTRS2017

Technology for NASA's Planetary Science Vision 2050.

NASAs Planetary Science Division (PSD) initiated and sponsored a very successful community Workshop held from Feb. 27 to Mar. 1, 2017 at NASA Headquarters. The purpose of the Workshop was to develop a vision of planetary science research and exploration for the next three decades until 2050. This abstract summarizes some of the salient technology needs discussed during the three-day workshop and at a technology panel on the final day. It is not meant to be a final report on technology to achieve the science vision for 2050.

Vision↗

DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗

DOE OSTI2024

Improvements in Optical Surface Measurement Using Reflected Computer Vision Targets

Since 2021, NREL has been developing a system to measure heliostats by measuring the deflection of printed computer vision targets, called the Reflected Target Non-intrusive Assessment (ReTNA) [6], [7]. While this system will have lower resolution than a fringe deflectometry system, it has several important advantages that make it a complimentary technology: 2D surface slope measurement can be generated from a single image, it can operate in ambient lighting, target points can be directly located in 3D space with photogrammetry allowing for a non-flat target, and it's well suited to using a smaller target, and multiple images to measure larger optical surfaces. ReTNA has undergone several significant changes and improvements, described below. This talk will summarize new system layouts designed for commercial use, new computer vision algorithms used to automate the analysis process and validation campaigns for the ReTNA software.

computer vision↗

DOE OSTI2025

Advanced Fuels Campaign: Strategic Vision

The Advanced Fuels Campaign (AFC) is dedicated to propelling the United States to the forefront of nuclear fuel technology through a comprehensive strategic vision. The vision is structured around five key goals: driving U.S. leadership in nuclear fuel technology, expanding nuclear energy production from the existing fleet, completing the qualification basis for advanced reactor fuel technology, driving innovation in advanced nuclear fuel technology, and enabling a high-performing organization. By focusing on these goals, AFC aims to define and implement cutting-edge nuclear fuel technologies, support industry advancements, achieve critical fuel qualifications, foster innovative research, and ensure effective program management and stakeholder engagement. This strategic approach will solidify the United States’ position as a global leader in nuclear energy and fuel technology, while also preparing for the future of environmentally friendly and reliable nuclear power.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Advanced Fuels Campaign: Strategic Vision

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multichannel meta-imagers for accelerating machine vision

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. Further, in this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗