FDnet:基于频域分解网络的红外小目标检测OA

In recent years,the detection of small infrared targets,which lack texture and shape information,in the presence of complex background clutter has become a significant challenge.Traditional model-driven approaches exhibit limited feature-learning and representation capabilities,thus showing poor adaptability to diverse scenarios.Most deep-learning-based detection methods rely on deep network architectures to extract features;such architectures may lead to the loss of fine-grained texture information in deeper layers and are thus less effective for small infrared target detection.To address these challenges,we propose a frequency decomposition network(FDnet)that follows the design principle of decomposing an image in the frequency domain and processing different frequency components separately.Specifically,FDnet first employs a high-frequency feature extraction module to decompose the input image into high-and low-frequency components.These components are then processed by two separate branches to extract boundary and semantic information.To facilitate interaction between the two branches,a spatial information aggregation(SIA)module is introduced,enabling high-frequency features to guide the low-frequency branch.Furthermore,considering the sparsity of high-frequency components,a spatially sparse self-attention mechanism(SSAM)is incorporated into the high-frequency branch to better capture spatial attention,whereas a channel-wise attention mechanism(CAM)is embedded in the low-frequency branch to model global channel dependencies.These components operate collectively to enhance a network's perception of meaningful targets.Experimental results on public datasets demonstrate that the proposed method achieves high detection accuracy with significantly fewer parameters compared to other state-of-the-art approaches.