The paper presents the Gradient Attention Network (GaNet) for efficient infrared small target detection (IRSTD). The key innovations are:
Gradient Transformer (GradFormer) module: This module simulates central difference convolutions (CDC) to extract and integrate gradient features with deeper features, enabling the network to learn a comprehensive feature representation of the target.
Global Feature Extraction Module (GFEM): This module solves the problem of lacking global background perception, improving the ability to obtain contextual information. It employs non-local attention and squeeze-and-excitation blocks to capture spatial and channel-wise global features.
The authors conduct extensive experiments on the NUDT-SIRST and IRSTD-1K datasets, demonstrating that GaNet outperforms state-of-the-art methods in terms of metrics like mean Intersection over Union (mIoU), F-measure (F1), probability of detection (Pd), and false alarm rate (Fa). The proposed network achieves these results with significantly fewer parameters compared to other complex models.
The ablation studies verify the effectiveness of the GradFormer and GFEM modules, highlighting the importance of extracting gradient information and integrating global context for IRSTD. The qualitative and quantitative results show that GaNet can effectively detect small and dim infrared targets in complex backgrounds, making it a promising solution for various space-based computer vision applications.
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by Chen Hu, Yia... о arxiv.org 10-01-2024
https://arxiv.org/pdf/2409.19599.pdfГлибші Запити