An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

Sachan, Rishabh; Kundra, Suryansh; Dubey, Ashwani Kumar

doi:10.1109/icepe55035.2022.9798144

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…A separate research study recently used LWIR imagery to train an object detection model that achieved an average accuracy of 91.9% during periods of limited visibility 21 . However, it was identi ed that a shortfall of LWIR object detection is that LWIR cameras have di culty identifying object classes at larger distance.…”

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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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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Analysis of mango fruit surface temperature using thermal imaging and deep learning

Pathmanaban¹,

Gnanavel²,

Anandan³

et al. 2023

International Journal of Food Engineering

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Thermal imaging has the potential to measure the object’s surface temperature. This study investigated the thermal behavior of mango fruit stored in a refrigerated environment. Thermal images of the fruit were collected with sufficient quality by supplying hot air to the acquisition environment. Grey-Level Co-occurrence Matrix (GLCM) features of mango images were determined to distinguish the subtle and noticeable changes. The thermal images were analyzed to find the temperature difference between the different regions of the fruit. The temperature of the bruise boundary (T bd ) was higher than the bruised center (T C ) throughout the storage period. In addition, an enhanced deep-learning model was used to predict the damaged mango. Over 10 days, 3500 thermal images were obtained from the 400 mangoes. In that, 80 % of the images were used for training, 10 % for testing, and 10 % for validation. The model achieved a classification accuracy of 99.6 %.

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An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

Cited by 3 publications

References 27 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

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

Analysis of mango fruit surface temperature using thermal imaging and deep learning

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