Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework

TL;DR

This paper introduces the FEST framework that enhances infrared small target detection by improving feature perception and adjusting target confidence, significantly boosting detection performance in complex scenarios.

Contribution

The FEST framework is a novel, compatible approach that enhances existing SIRST detection networks through multi-scale feature fusion, edge enhancement loss, and an adjustable sensitivity strategy.

Findings

Improves detection rate of infrared small targets.

Enhances robustness against complex scenarios.

Compatible with existing detection networks.

Abstract

Recently, single-frame infrared small target (SIRST) detection technology has attracted widespread attention. Different from most existing deep learning-based methods that focus on improving network architectures, we propose a feature-enhanced and sensitivity-tunable (FEST) framework, which is compatible with existing SIRST detection networks and further enhances their detection performance. The FEST framework improves the model's robustness from two aspects: feature enhancement and target confidence regulation. For feature enhancement, we employ a multi-scale fusion strategy to improve the model's perception to multi-scale features of multi-size targets, and design an edge enhancement difficulty mining (EEDM) loss to guide the network to continuously focus on challenging target regions and edge features during training. For target confidence regulation, an adjustable sensitivity (AS) strategy is proposed for network post-processing. This strategy enhances the model's adaptability in complex scenarios and significantly improves the detection rate of infrared small targets while maintaining segmentation accuracy. Extensive experimental results show that our FEST framework can effectively enhance the performance of existing SIRST detection networks. The code is available at https://github.com/YuChuang1205/FEST-Framework