DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection

2024-09-29

AI Summary

To address the challenge of detecting weak and small infrared targets that are easily obscured by complex backgrounds, this paper proposes the Dynamic Attention Transformer (DATrans). DATrans first employs center-difference convolution to model edge-gradient features, then adaptively fuses multi-scale gradient representations with deep semantic features via a dynamic attention mechanism. Furthermore, a Global Background-Aware Module (GBAM) is introduced to explicitly model target-background contextual relationships, thereby enhancing discriminability. The architecture achieves a synergistic optimization of fine-grained detail sensitivity and global semantic understanding while maintaining computational efficiency. Extensive experiments on multiple benchmark infrared small-target datasets demonstrate significant improvements over state-of-the-art methods in both detection accuracy and robustness. The source code is publicly available.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds.To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve edge information of small targets.DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract and integrate gradient features with deeper features.Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the background information. We compare the network with state-of-the-art (SOTA) approaches, and the results demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.

Problem

• Infrared Imaging
• Small Target Detection
• Complex Backgrounds

Innovation

• DATransNet
• Dynamic Attention Transformer
• Global Feature Extraction Module