Triple-Directional Fusion Attention for Infrared Small Target Detection

Attention mechanism has gained popularity due to its effectiveness. However, most existing mechanisms are designed for large-sized targets currently, with limited improvement in single-frame infrared small target (SIRST) detection tasks. In this letter, we propose a novel attention mechanism to enhance the extraction capacity of deep networks for infrared small targets, termed the triple-directional fusion attention module (TFAM). This module aggregates channel, height, and width dimension into three independent directional perception attention vectors, preserving both accurate channel and spatial information. Through adaptive cross-direction interaction, TFAM establishes interdirectional dependencies essential for enhancing faint target signatures in deep layers. Notably, TFAM only requires minimal complexity for modeling and offers flexibility in integration. Experiments conducted on the NUDT-SIRST and NUAA-SIRST datasets demonstrate consistent improvements.