A fast and robust automatic object detection algorithm to detect small objects in infrared images

Ozbay, Muhammet; Sahingil, Mehmet Cihan

doi:10.1109/siu.2017.7960456

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Generally, people use simple long-distance detection methods to detect tiny targets, but there is still the problem that the signal-to-noise ratio (SNR) gradually decreases leading to a decrease in the ability to distinguish objects for recognition, so a more complex algorithm is needed to solve the problem. Researchers have proposed an algorithm that can operate at high detection rates and low signal-tonoise ratios to automatically detect tiny objects in three-dimensional infrared images, as shown in fig 8 [11]. In this algorithm, the first task is to enable a generalized thresholding algorithm using a background suppression method to preprocess the image to improve the differentiation of low signalto-noise objects from the background.…”

Section: Tiny Target Detection In Infrared Imagesmentioning

confidence: 99%

Research on Methods of Micro-target Detection in Various Application Scenarios

Fang,

Jiao,

Wang

et al. 2024

HSET

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This paper provides a comprehensive introduction and comparison of the research on small target detection algorithms in different application scenarios and systematically explores the application of deep learning algorithms for small target detection in remote sensing imagery, visible imagery, and infrared imagery. In remote sensing images, the Deconvolution R-CNN algorithm is used to improve the accuracy of small target detection by adding a deconvolution layer. In the field of visible images, a Gaussian Mixture Model (GMM)-based approach is introduced to achieve fast detection of small targets with SIFT features and a GMM target detector. In infrared images, a comprehensive algorithm adapted to low signal-to-noise ratio and complex background is proposed to automatically detect tiny targets through background suppression and threshold selection optimization. In this paper, we compare and analyze the performance of these methods in different scenarios, and propose directions for future research, including expanding the training dataset, applying super-resolution methods to improve the image resolution, and enhancing the universality of the algorithms. This paper provides a comprehensive overview of research in the field of small target detection, as well as an outlook on future research directions, which provides a useful reference for the further development of the field.

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Section: Tiny Target Detection In Infrared Imagesmentioning

confidence: 99%

Research on Methods of Micro-target Detection in Various Application Scenarios

Fang,

Jiao,

Wang

et al. 2024

HSET

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“…Secondly, LWIR sensors are superior at segmenting the object of interest from the image background ('edge detection') [16], provided that the object of interest is radiating a thermal signature (as illustrated visually already in Figure 1). LWIR object detection is regularly adopted in military and homeland security use cases to detect illicit activity and identify targets, especially at night 18, 19 . However, most infrared (IR) sensors for military and national security applications use near-infrared (NIR), which operates between .75 -1.3 µm and does not work well for drone-based ML object detection models 20 .…”

Section: Literature Reviewmentioning

confidence: 99%

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

Gallagher¹,

Oughton²

2023

Preprint

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<p>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 LWIR-RGB 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. </p>

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“…1). LWIR object detection is regularly adopted in military and homeland security use cases to detect illicit activity and identify targets, especially at night 18,19 . However, most infrared (IR) sensors for military and national security applications use near-infrared (NIR), which operates between .75-1.3 µm and does not work well for drone-based ML object detection models 20 .…”

Section: Literature Reviewmentioning

confidence: 99%

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

Gallagher

Oughton

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

A fast and robust automatic object detection algorithm to detect small objects in infrared images

Cited by 10 publications

References 4 publications

Research on Methods of Micro-target Detection in Various Application Scenarios

Research on Methods of Micro-target Detection in Various Application Scenarios

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

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