Enhancing Infrared Small Target Detection with Background Semantics: The BAFE-Net Approach and DenseSIRST Dataset
Background semantics are crucial for accurately detecting small, clustered targets in infrared images, and the proposed BAFE-Net, trained on the novel DenseSIRST dataset, leverages this contextual information to significantly improve detection accuracy and reduce false alarms.
Improving Infrared Small Target Detection with Self-Supervised Learning and A Contrario Reasoning
Combining self-supervised learning and a contrario reasoning within a YOLO object detection framework significantly improves infrared small target detection, particularly in challenging conditions with limited data.
Learning Infrared Small Target Detection with Single Point Supervision: A Novel Label Evolution Framework
This paper introduces a novel label evolution framework called LESPS (Label Evolution with Single Point Supervision) to address the challenge of efficient and accurate infrared small target detection using only point-level supervision, significantly reducing annotation costs while achieving comparable performance to fully supervised methods.
Selective Rank-Aware Attention Network (SeRankDet) for Infrared Small Target Detection
SeRankDet, a novel deep learning architecture, achieves superior infrared small target detection by employing selective rank-aware attention and dynamic feature fusion to overcome the limitations of traditional methods and enhance target-background separation.
Gradient-Based Attention Fusion for Efficient Infrared Small Target Detection
The proposed Gradient Attention Network (GaNet) effectively extracts and preserves edge and gradient information of small infrared targets, while a global feature extraction module provides comprehensive background perception to improve detection performance.
Spatial-channel Cross Transformer Network for Accurate Infrared Small Target Detection
The proposed Spatial-channel Cross Transformer Network (SCTransNet) leverages spatial-channel cross transformer blocks to effectively model the semantic differences between infrared small targets and complex backgrounds, enabling accurate detection of small targets.
Enhancing Infrared Small Target Detection with Scale and Location Sensitivity
A novel Scale and Location Sensitive (SLS) loss is proposed to improve infrared small target detection by addressing the limitations of existing loss functions in capturing scale and location information. A simple Multi-Scale Head is introduced to the plain U-Net (MSHNet) to leverage the SLS loss, achieving state-of-the-art performance without complex model structures.
IRSTD-Diff: Generative Approach for Infrared Small Target Detection
Proposing a diffusion model framework for IRSTD to address target-level insensitivity and designing a low-frequency isolation module in the wavelet domain.
Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling
The author proposes a diffusion model framework for Infrared Small Target Detection to address target-level insensitivity by generating mask posterior distributions.
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