AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection
This addresses the problem of detecting small targets in infrared images for applications like earth observation and military reconnaissance, representing an incremental improvement.
The paper tackles infrared small target detection by proposing AGPCNet, which uses attention-guided pyramid context networks to integrate multi-scale features, achieving new state-of-the-art performance on two infrared datasets.
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.