高阶微分方程启发的红外小目标检测网络

Infrared Small Target Detection Network Inspired by High-Order Differential Equation

红外小目标检测广泛应用于红外检测、红外跟踪等诸多实际领域,但红外小目标检测难度较大,现有红外小目标检测方法不能解决复杂背景问题,并且在特征提取中容易丢失细节信息.因此,文中提出高阶微分方程启发的红外小目标检测网络.在可解释的理论指导下设计四阶Adams引导的特征融合模块,引入自适应权重因子,有效融合不同层级的多尺度信息,并将求解的高阶差分方程应用于网络,通过深层次的学习消除冗杂信息.目标特征增强模块使用不同尺度卷积构成的残差结构,旨在对原始特征进行抑制背景噪声和增强信息量大的多尺度特征操作.在公开数据集SIRST上的小目标检测实验表明,文中网络检测结果的多个评估指标值以及视觉效果均较优.

In the fields of infrared detection and infrared tracking,infrared small target detection is widely applied.However,infrared small target detection poses significant challenges.The existing methods for infrared small target detection fail to address complex background issues while losing detailed information during feature extraction.Therefore,an infrared small target detection network inspired by high-order differential equations is proposed.Under the guidance of the interpretable theory,a fourth-order Adams-guided feature fusion module is designed,incorporating adaptive weight factors to effectively fuse multi-scale information from different levels.High-order difference equations are employed to eliminate redundant information through deep learning.The target feature enhancement module utilizes a residual structure composed of convolutions at different scales to suppress background noise and enhance multi-scale features with high information content.Experiments for small target detection on publicly available SIRST dataset show that the proposed network has advantages in the evaluation metrics and visual quality.