Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)

Key points

• First valuable research has emerged since 30 years ago and the two last decades witnessed very significant publications in this field following the rise of low-rank and sparse decomposition (model-based methods) and deep learning models (data-based methods).1
• In this context, the aim of this paper is to provide a complete survey of the traditional and recent approaches in infrared small target detection.1
• First, we categorize the different approaches found in the literature in terms of the modeling models, i.e mathematical models, machine learning models and signal processing models.1
• Furthermore, we present the available resources, datasets and libraries.1

Sources (1)

• [1]Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 12:31 PM

  Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in ground, air and sea surveillance.

  First valuable research has emerged since 30 years ago and the two last decades witnessed very significant publications in this field following the rise of low-rank and sparse decomposition (model-based methods) and deep learning models (data-based methods).

Extractive summary: sentences quoted from the sources.