Small and Dim Target Detection in IR Imagery: A Review
- URL: http://arxiv.org/abs/2311.16346v1
- Date: Mon, 27 Nov 2023 22:25:46 GMT
- Title: Small and Dim Target Detection in IR Imagery: A Review
- Authors: Nikhil Kumar, Pravendra Singh
- Abstract summary: This is the first review in the field of small and dim target detection in infrared imagery.
There are two main types of approaches: methodologies using several frames for detection, and single-frame-based detection techniques.
Our findings indicate that deep learning approaches perform better than traditional image processing-based approaches.
- Score: 9.941925002794262
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: While there has been significant progress in object detection using
conventional image processing and machine learning algorithms, exploring small
and dim target detection in the IR domain is a relatively new area of study.
The majority of small and dim target detection methods are derived from
conventional object detection algorithms, albeit with some alterations. The
task of detecting small and dim targets in IR imagery is complex. This is
because these targets often need distinct features, the background is cluttered
with unclear details, and the IR signatures of the scene can change over time
due to fluctuations in thermodynamics. The primary objective of this review is
to highlight the progress made in this field. This is the first review in the
field of small and dim target detection in infrared imagery, encompassing
various methodologies ranging from conventional image processing to
cutting-edge deep learning-based approaches. The authors have also introduced a
taxonomy of such approaches. There are two main types of approaches:
methodologies using several frames for detection, and single-frame-based
detection techniques. Single frame-based detection techniques encompass a
diverse range of methods, spanning from traditional image processing-based
approaches to more advanced deep learning methodologies. Our findings indicate
that deep learning approaches perform better than traditional image
processing-based approaches. In addition, a comprehensive compilation of
various available datasets has also been provided. Furthermore, this review
identifies the gaps and limitations in existing techniques, paving the way for
future research and development in this area.
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