AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection

TL;DR

AGPCNet introduces a novel neural network architecture with attention-guided modules for improved infrared small target detection, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper proposes AGPCNet, a new attention-guided pyramid network with specialized modules for enhanced small target detection in infrared imagery.

Findings

Achieved state-of-the-art performance on infrared small target datasets.

Demonstrated effectiveness of attention-guided modules in feature enhancement.

Provided open-source code for reproducibility.

Abstract

Infrared small target detection is an important problem in many fields such as earth observation, military reconnaissance, disaster relief, and has received widespread attention recently. This paper presents the Attention-Guided Pyramid Context Network (AGPCNet) algorithm. Its main components are an Attention-Guided Context Block (AGCB), a Context Pyramid Module (CPM), and an Asymmetric Fusion Module (AFM). AGCB divides the feature map into patches to compute local associations and uses Global Context Attention (GCA) to compute global associations between semantics, CPM integrates features from multi-scale AGCBs, and AFM integrates low-level and deep-level semantics from a feature-fusion perspective to enhance the utilization of features. The experimental results illustrate that AGPCNet has achieved new state-of-the-art performance on two available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.