To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we des...To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps.To improve the adaptability of the network for targets of different sizes,we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism.Based on the research on human vision system,we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets.With this parameterized component,we can implement the information aggregation of multi-scale contrast saliency maps.Finally,to fully utilize the information within spatial and channel domains in feature maps of different scales,we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers.Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union,Precision,Recall,and F-measure.In addition,we conduct detailed ablation studies to verify the effectiveness of each component in our network.展开更多
基金the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2021-5)。
文摘To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps.To improve the adaptability of the network for targets of different sizes,we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism.Based on the research on human vision system,we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets.With this parameterized component,we can implement the information aggregation of multi-scale contrast saliency maps.Finally,to fully utilize the information within spatial and channel domains in feature maps of different scales,we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers.Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union,Precision,Recall,and F-measure.In addition,we conduct detailed ablation studies to verify the effectiveness of each component in our network.
