PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection

Abstract

Given the widespread influence of U-Net and FPN network architectures on infrared small target detection tasks on existing models, these structures frequently incorporate a significant number of downsampling operations, thereby rendering the preservation of small target information and contextual interaction both challenging and computation-consuming. To tackle these challenges, we introduce a parallel connected lateral chain network (PCLC-Net), an innovative architecture in the domain of infrared small target detection, that preserves large-scale feature maps while minimizing downsampling operations. The PCLC-Net preserves large-scale feature maps to prevent small target information loss, integrates causal-based retention gates (CBR Gates) within each chain for improved feature selection and fusion, and leverages the attention-based network-wide feature map aggregation (AN-FMA) output module to ensure that all feature maps abundant with small target information contribute effectively to the model’s output. The experimental results reveal the PCLC-Net, with minimal nodes and just a single downsampling, achieves near state-of-the-art performance using just 0.16M parameters (40% of the current smallest model), yielding an $IoU$ of 80.8%, $P_d$ of 95.1%, and $F_a$ of $28.6\times {10}^{-6}$ on the BIT-SIRST dataset.