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

• URL: http://arxiv.org/abs/2506.02393v2
• Date: Tue, 24 Jun 2025 08:09:15 GMT
• Title: RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection
• Authors: Yongxian Liu, Boyang Li, Ting Liu, Zaiping Lin, Wei An,
• Abstract summary: Infrared small target detection is a challenging task due to its unique characteristics.<n>Recent CNN-based methods have achieved promising performance with heavy feature extraction and fusion modules.<n>We propose a recurrent reusable-convolution attention network (RRCA-Net) for infrared small target detection.
• Score: 23.54800619558163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Our code will be available at https://github.com/yongxianLiu/ soon.

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