Deep learning based infrared small object segmentation: Challenges and future directions

TL;DR

This paper reviews deep learning techniques for infrared small object segmentation, discusses current challenges like low signal-to-noise ratios and limited data, and outlines future research directions to improve infrared perception systems.

Contribution

It provides a comprehensive survey of existing deep learning methods for infrared small object segmentation, highlighting challenges and proposing future research directions.

Findings

Infrared images have low signal-to-noise ratios and small objects.

Limited labeled data hampers deep learning performance.

Existing methods show varied success in detection accuracy.

Abstract

Infrared sensing is a core method for supporting unmanned systems, such as autonomous vehicles and drones. Recently, infrared sensors have been widely deployed on mobile and stationary platforms for detection and classification of objects from long distances and in wide field of views. Given its success in the vision image analysis domain, deep learning has also been applied for object recognition in infrared images. However, techniques that have proven successful in visible light perception face new challenges in the infrared domain. These challenges include extremely low signal-to-noise ratios in infrared images, very small and blurred objects of interest, and limited availability of labeled/unlabeled training data due to the specialized nature of infrared sensors. Numerous methods have been proposed in the literature for the detection and classification of small objects in infrared images achieving varied levels of success. There is a need for a survey paper that critically analyzes existing techniques in this domain, identifies unsolved challenges and provides future research directions. This paper fills the gap and offers a concise and insightful review of deep learning-based methods. It also identifies the challenges faced by existing infrared object segmentation methods and provides a structured review of existing infrared perception methods from the perspective of these challenges and highlights the motivations behind the various approaches. Finally, this review suggests promising future directions based on recent advancements within this domain.