Small and Dim Target Detection in IR Imagery: A Review

TL;DR

This review summarizes recent progress in small and dim target detection in IR imagery, highlighting the superiority of deep learning methods over traditional approaches and providing a taxonomy and dataset compilation.

Contribution

First comprehensive review of small and dim IR target detection methods, introducing a taxonomy and analyzing the performance of various approaches including deep learning.

Findings

Deep learning outperforms traditional image processing methods.

Two main detection approaches: multi-frame and single-frame techniques.

A compilation of available IR datasets is provided.

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.