Single-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small target feature loss and positional offset during continuous downsampling; at the same time, the sparse features of the small targets do not correlate well with the global-local linkage of the background features. To solve the above problems, this paper proposes an efficient infrared small target detection method. First, this paper incorporates BlurPool in the feature extraction part, which reduces the loss and positional offset of small target features in the process of convolution and pooling. Second, this paper designs an interactive attention deep feature fusion module, which acquires the correlation information between the target and the background from a global perspective, and designs a compression mechanism based on deep a priori knowledge, which reduces the computational difficulty of the self-attention mechanism. Then, this paper designs the context local feature enhancement and fusion module, which uses deep semantic features to dynamically guide shallow local features to realize enhancement and fusion. Finally, this paper proposes an edge feature extraction module for shallow features, which utilizes the complete texture and location information in the shallow features to assist the network to initially locate the target position and edge shape. Numerous experiments show that the method in this paper significantly improves nIoU, F1-Measure and AUC on IRSTD-1k Datasets and NUAA-SIRST Datasets.

单帧红外小目标检测受图像分辨率低、目标尺寸小的影响，在连续下采样时容易出现小目标特征丢失和位置偏移的问题；同时，小目标的稀疏特征与背景特征的全局局部联系没有很好的相关性。针对上述问题，本文提出一种高效的红外小目标检测方法。首先，本文在特征提取部分加入了BlurPool，减少了小目标特征在卷积和池化过程中的损失和位置偏移。其次，本文设计了交互式注意力深度特征融合模块，从全局角度获取目标与背景之间的相关性信息，并设计了基于深层先验知识的压缩机制，降低了自学习的计算难度。注意机制。然后，本文设计了上下文局部特征增强和融合模块，利用深层语义特征动态引导浅层局部特征实现增强和融合。最后，本文提出了一种针对浅层特征的边缘特征提取模块，利用浅层特征中完整的纹理和位置信息来辅助网络初步定位目标位置和边缘形状。大量实验表明，本文方法在IRSTD-1k数据集和NUAA-SIRST数据集上显着提高了nIoU、F1-Measure和AUC。