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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Computer vision techniques for rotorcraft low altitude flight

Rotorcraft operating in high-threat environments fly close to the earth's surface to utilize surrounding terrain, vegetation, or manmade objects to minimize the risk of being detected by an enemy. Increasing levels of concealment are achieved by adopting different tactics during low-altitude flight. Rotorcraft employ three tactics during low-altitude flight: low-level, contour, and nap-of-the-earth (NOE). The key feature distinguishing the NOE mode from the other two modes is that the whole rotorcraft, including the main rotor, is below tree-top whenever possible. This leads to the use of lateral maneuvers for avoiding obstacles, which in fact constitutes the means for concealment. The piloting of the rotorcraft is at best a very demanding task and the pilot will need help from onboard automation tools in order to devote more time to mission-related activities. The development of an automation tool which has the potential to detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles, presents challenging problems. Research is described which applies techniques from computer vision to automation of rotorcraft navigtion. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle-detection approach can be used as obstacle data for the obstacle avoidance in an automatic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. The presentation concludes with some comments on future work and how research in this area relates to the guidance of other autonomous vehicles.

Sridhar, Banavar↗
DOE OSTI2024

Computer Vision on Edge Devices for the Short Term Prediction of Cloud Cover

Edge Computing and IoT are important pieces of today's technological landscape. Here, we build a low-cost IoT sensor for sky imaging and program it using AWS GreenGrass, one of the leading IoT platforms. We demonstrate remote reprogramming of this device to load software that predicts sun shading events through the linear advection method, which is a baseline algorithm that can be used to benchmark algorithmic improvements in future work. Some future directions for sky imaging research are enumerated.

14 SOLAR ENERGY↗
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↗
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 OSTI2026

High-throughput methods leveraging robotics and computer vision for the development of therapeutic phage cocktails

We present the high-throughput automated screening techniques that are being used to develop bacteriophage-based therapeutic products currently under investigation in human clinical trials to combat urinary tract infections. By integrating modern liquid handling robotics, standardized phenotypic assays, and computer vision-based enumeration, we established a platform capable of reproducibly screening large collections of phages against clinically derived bacterial strain panels. This approach enabled systematic assessment of phage-bacteria interactions at scale, facilitating the identification and optimization of phage cocktails with broad in vitro activity. Although bacteriophage therapy has long been investigated as a strategy for treating bacterial infections, few frameworks exist for developing phage combinations in a reproducible and scalable manner. The methods outlined here address this gap and aim to support the broader development of therapeutic assets available to combat antibiotic resistance.

Penke, Taylor J. R. [Locus Biosciences, Morrisvill↗
DOE OSTI2025

An open retail boundary dataset for South Korea using open data and computer vision technique

Although delineating retail boundaries is important to explore and comprehend the dynamics of the retail sector, it is hard to find studies specifically addressing it in the South Korean context. This study fills this gap by proposing new retail boundaries across South Korea. To achieve this goal, we employed a variety of retailers and building datasets and proposed a unique computer vision-based framework with a deep ensemble voting technique. As a result, we delineated 6,636 distinct retail boundaries that were validated against existing reference retail boundaries. These newly delineated retail boundaries provide valuable insights for researchers, governments, and other relevant stakeholders by enhancing their understanding of retail geography. This dataset can be used as a foundational resource for analyses on topics such as pandemic recovery, retail gentrification, and the resilience of retail spaces in response to e-commerce growth, ultimately contributing to more robust retail sector research in South Korea.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy

Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dynamically evolve during the switching processes, making them a challenge to characterize via traditional techniques. Here, we implement an automated experimentation approach for the exploration of functional properties in BiFeO 3 thin films. Specifically, we visualize the ferroelectric domain structures via single frequency piezoresponse force microscopy and implement a computer vision-based algorithm to discover features of interest at which spectroscopic measurements are taken. Subsequently, we employ dimensionality reduction techniques to reveal characteristic polarization behaviors at these features. This approach can be extended to other spectroscopies and modalities to probe only specific features of interest, ultimately enabling dynamical processes in ferroelectrics to be studied.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computer Vision: EO Imaging & Video Tasks, Metrics, and Datasets [Slides]

Classification is an assignment of an image to a certain category. Localization is identified by the box surrounding the object in the image. Object detection detects instances of semantic objects of a certain class. Semantic segmentation categorizes all the pixels of an image into classes of objects. Instance segmentation identifies each instance of each object in the image. Panoptic (i.e., showing or seeing everything at once) segmentation is a combination of semantic and instance segmentation and classifies all the pixels in the image.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗
DOE OSTI2024

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA

Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.

58 GEOSCIENCES↗
NASA NTRS2007

State-Estimation Algorithm Based on Computer Vision

An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.

Bayard, David↗
NASA NTRS1994

The use of interactive computer vision and robot hand controllers for enhancing manufacturing safety

Current available robotic systems provide limited support for CAD-based model-driven visualization, sensing algorithm development and integration, and automated graphical planning systems. This paper describes ongoing work which provides the functionality necessary to apply advanced robotics to automated manufacturing and assembly operations. An interface has been built which incorporates 6-DOF tactile manipulation, displays for three dimensional graphical models, and automated tracking functions which depend on automated machine vision. A set of tools for single and multiple focal plane sensor image processing and understanding has been demonstrated which utilizes object recognition models. The resulting tool will enable sensing and planning from computationally simple graphical objects. A synergistic interplay between human and operator vision is created from programmable feedback received from the controller. This approach can be used as the basis for implementing enhanced safety in automated robotics manufacturing, assembly, repair and inspection tasks in both ground and space applications. Thus, an interactive capability has been developed to match the modeled environment to the real task environment for safe and predictable task execution.

Marzwell, Neville I.↗
NASA NTRS1983

Information theory analysis of sensor-array imaging systems for computer vision

Information theory is used to assess the performance of sensor-array imaging systems, with emphasis on the performance obtained with image-plane signal processing. By electronically controlling the spatial response of the imaging system, as suggested by the mechanism of human vision, it is possible to trade-off edge enhancement for sensitivity, increase dynamic range, and reduce data transmission. Computational results show that: signal information density varies little with large variations in the statistical properties of random radiance fields; most information (generally about 85 to 95 percent) is contained in the signal intensity transitions rather than levels; and performance is optimized when the OTF of the imaging system is nearly limited to the sampling passband to minimize aliasing at the cost of blurring, and the SNR is very high to permit the retrieval of small spatial detail from the extensively blurred signal. Shading the lens aperture transmittance to increase depth of field and using a regular hexagonal sensor-array instead of square lattice to decrease sensitivity to edge orientation also improves the signal information density up to about 30 percent at high SNRs.

Huck, F. O.↗