Recent advances in multi-INT track fusion

Coraluppi, Stefano; Carthel, Craig; Kreamer, William; Willsky, Alan S.

doi:10.1109/aero.2016.7500540

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Additionally, other GAN-based methods have been proposed by various researchers. [30][31][32] Due to the current limitations of end-to-end methods, such as blurry generation, limited preservation of details, and suboptimal fusion performance without proper constraints or sufficient training data, this study aims to improve upon existing GANbased fusion methods.…”

Section: Approaches Based On Deep Learningmentioning

confidence: 99%

“…In recent years, there has been a rapid advancement in IVIF benchmark datasets. These datasets include the TNO image fusion, 32 INO videos analytics, OSU color-thermal, RoadScene, 33 and multispectral 34 datasets. The TNO dataset stands out as the most widely used publicly available dataset for IVIF, encompassing 261 pairs of multispectral images captured during both daytime and nighttime.…”

Section: Benchmarksmentioning

confidence: 99%

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Infrared and visible image fusion method based on hierarchical attention mechanism

Li,

Yan,

Luo

2024

J. Electron. Imag.

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Currently, most feature extraction methods lack generality and require specific feature extraction methods for images from different sensors, particularly in the detection of faults in power equipment. However, the challenge lies in how to restore realistic texture details while correcting the color distortion. To address this issue, we propose an innovative infrared-visible light image fusion technique that combines hierarchical attention modules and collaborative refinement modules to facilitate feature fusion by jointly preserving intricate details and correcting lighting conditions. The hierarchical attention module aims to provide two different attention weight maps, which help select the most salient information from the source images in the infrared and visible light domains. This ultimately produces intermediate fusion results with comprehensive and complementary data. The collaborative refinement module consists of an edge enhancement network and a lighting correction network, which works together to enhance edge details and correct color differences. Experimental results validate the effectiveness of this method in successfully fusing the two types of images. The results demonstrate that, compared with several mainstream fusion methods, this method exhibits significant advantages in publicly available datasets and scenarios, such as power equipment detection.

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Section: Approaches Based On Deep Learningmentioning

confidence: 99%

Section: Benchmarksmentioning

confidence: 99%

Infrared and visible image fusion method based on hierarchical attention mechanism

Li,

Yan,

Luo

2024

J. Electron. Imag.

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“…The models will use multistage data fusion. This fuses measurements from a variety of sources (sensors and contextual data) and over a range of time epochs to generate a consolidated state history of the object being monitored [38]. The models will also mitigate data dependencies (physical dependencies) across the physical objects being monitored.…”

Section: Future Researchmentioning

confidence: 99%

Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey

Hodge

O’Keefe

Weeks

et al. 2015

IEEE Trans. Intell. Transport. Syst.

412

188

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In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

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