RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection

TL;DR

This paper introduces RRCA-Net, a recurrent attention network that efficiently detects small infrared targets by refining features and enhancing contextual information, achieving high performance with fewer parameters.

Contribution

The paper proposes a novel recurrent reusable-convolution attention network with a dual attention module and a characteristic loss function for improved infrared small target detection.

Findings

Achieves comparable performance to state-of-the-art methods.

Maintains high detection accuracy with fewer parameters.

Enhances existing methods as a plug-and-play module.

Abstract

Infrared small target detection is a challenging task due to its unique characteristics (e.g., small, dim, shapeless and changeable). Recently published CNN-based methods have achieved promising performance with heavy feature extraction and fusion modules. To achieve efficient and effective detection, we propose a recurrent reusable-convolution attention network (RRCA-Net) for infrared small target detection. Specifically, RRCA-Net incorporates reusable-convolution block (RuCB) in a recurrent manner without introducing extra parameters. With the help of the repetitive iteration in RuCB, the high-level information of small targets in the deep layers can be well maintained and further refined. Then, a dual interactive attention aggregation module (DIAAM) is proposed to promote the mutual enhancement and fusion of refined information. In this way, RRCA-Net can both achieve high-level feature refinement and enhance the correlation of contextual information between adjacent layers. Moreover, to achieve steady convergence, we design a target characteristic inspired loss function (DpT-k loss) by integrating physical and mathematical constraints. Experimental results on three benchmark datasets (e.g. NUAA-SIRST, IRSTD-1k, DenseSIRST) demonstrate that our RRCA-Net can achieve comparable performance to the state-of-the-art methods while maintaining a small number of parameters, and act as a plug and play module to introduce consistent performance improvement for several popular IRSTD methods.