Color Palette Selection In Thermal Imaging For Enhancing Situation Awareness During Detection-Recognition Tasks

Agrawal, D. P.; Karar, Vinod

doi:10.1109/icrieece44171.2018.9008486

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Thermal object detection is also advantageous because of the ability to conform an image to a desired color palette 32 , thereby reducing the overall number of colors compared to RGB images 33 . Often, RGB images can have backgrounds that blend in with the object of interest 34 , making object detection a more challenging task.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Gallagher

Oughton

2023

Preprint

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Object detection models commonly focus on utilizing the visible spectrum via Red-Green-Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) (7.5–13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation finding that a blended RGB-LWIR model generally exhibits superior performance compared to traditional RGB or LWIR approaches. For example, an RGB-LWIR blend only performed 1–5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlayed provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms. This approach has the ability to improve object detection performance for a range of use cases in industrial, consumer, government, and military applications. Finally, this research additionally contributes a novel open labeled training dataset of 6,300 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from air-based platforms, enabling further multispectral machine-driven object detection research.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Gallagher

Oughton

2023

Preprint

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“…With the proper color combination, the rendering of thermal images can be very effective and appealing. In a medical sense, color palette selection helps in the detection or recognition process [29]. With color palettes, facial thermal images can be captured and analyzed using mathematical algorithms to extract meaningful features [30].…”

Section: Color Palettes For Thermal Imagingmentioning

confidence: 99%

Evaluating Infrared Thermal Image’s Color Palettes in Hot Tropical Area

Olalia¹,

Olalia²,

Carse³

2021

JCC

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Assessing thermal imagery integration into object detection methods on air-based collection platforms

Gallagher

Oughton

2023

Sci Rep

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Object detection models commonly focus on utilizing the visible spectrum via Red–Green–Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal Long Wave Infrared (LWIR) (7.5–13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation, finding that a blended RGB-LWIR model generally exhibits superior performance compared to independent RGB or LWIR approaches. For example, an RGB-LWIR blend only performs 1–5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlay provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms (especially in low visibility environments). This approach has the ability to improve object detection performance for a range of use cases in industrial, consumer, government, and military applications. This research greatly contributes to the study of multispectral object detection by quantifying key factors affecting model performance from drone platforms (including distance, time-of-day and sensor type). Finally, this research additionally contributes a novel open labeled training dataset of 6300 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from air-based platforms, enabling further multispectral machine-driven object detection research.

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Color Palette Selection In Thermal Imaging For Enhancing Situation Awareness During Detection-Recognition Tasks

Cited by 3 publications

References 12 publications

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Evaluating Infrared Thermal Image’s Color Palettes in Hot Tropical Area

Assessing thermal imagery integration into object detection methods on air-based collection platforms

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