Abstract:

An infrared small target method based on information compensation was proposed to address the problem that infrared small targets are prone to losing texture detail information during network iteration， which decreased accuracy of target localization and contour segmentation. Firstly， Image Feature Extraction （IFE） module was used to encode shallow details and deep semantic features of infrared image. Secondly， a Multi-level Information Compensation （MIC） module was constructed to perform information compensation to down-sampled features in the encoding stage by aggregating features from adjacent levels. Thirdly， Global Target Response （GTR） module was introduced to compensate the limitation of convolutional locality by incorporating global contextual information of feature map. Finally， Asymmetric Cross-Fusion （ACF） module was constructed to fuse shallow and deep features， thereby preserving texture and positional information during target decoding， thus achieving detection of infrared small targets. Experimental results of training and testing on publicly available NUAA-SIRST （Nanjing University of Aeronautics and Astronautics-Single-frame InfraRed Small Target） and NUDT-SIRST （National University of Defense Technology-Single-frame InfraRed Small Target） mixed datasets show that compared to methods such as UIUNet （U-Net in U-Net Network）， LSPM （Local Similarity Pyramid Modules）， and DNANet （Dense Nested Attention Network）， the proposed method achieves improvements of 9.2， 8.9， and 5.5 percentage points in Intersection over Union （IoU）， respectively， and 6.0， 5.4， and 3.1 percentage points in F1-Score， respectively. The above demonstrates that the proposed method enables accurate detection and effective segmentation of small targets in complex infrared background images.

Key words: target detection, infrared small target, information compensation, Global Target Response (GTR), Asymmetric Cross-Fusion (ACF)

摘要：

针对红外弱小目标容易在网络迭代过程中损失纹理细节信息，从而导致目标定位和轮廓分割的准确性下降的问题，提出一种基于信息补偿的红外弱小目标检测方法。首先，利用图像特征提取（IFE）模块编码红外源图像的浅层细节及深层语义特征；其次，构建多级信息补偿（MIC）模块通过聚合相邻级别的特征对编码阶段下采样后的特征进行信息补偿；随后，引入全局目标响应（GTR）模块联合特征图的全局上下文信息对卷积局部性的限制进行补偿；最后，构建非对称交叉融合（ACF）模块对浅层和深层特征进行融合，以实现目标解码时纹理信息与位置信息的保留，进而完成对红外弱小目标的检测。在公开的NUAA-SIRST （Nanjing University of Aeronautics and Astronautics-Single-frame InfraRed Small Target）和NUDT-SIRST （National University of Defense Technology-Single-frame InfraRed Small Target）混合数据集上训练和测试的实验结果表明，与UIUNet （U-Net in U-Net Network）、LSPM （Local Similarity Pyramid Modules）和DNANet （Dense Nested Attention Network）等方法相比，所提方法在交并比（IoU）上分别提高了9.2、8.9和5.5个百分点，在F1分数（F1-Score）上分别提高了6.0、5.4和3.1个百分点。以上表明所提方法对红外复杂背景图像中的弱小目标可以实现准确检测和有效分割。

关键词: 目标检测, 红外弱小目标, 信息补偿, 全局目标响应, 非对称交叉融合