面向运动红外小目标检测的具有频率感知记忆增强的三域特征学习
Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection
AI总结：
针对运动红外小目标检测，本文提出融合时空频三域特征的 Tridos，通过频率感知、记忆增强与残差补偿提升性能，并在三个数据集上验证其优于现有方法。
摘要：
作为目标检测的一个子领域，运动红外小目标检测由于目标尺寸极小且相对于背景的对比度较低而面临重大挑战。现有方法主要依赖仅从时空域提取的特征。尽管频域已在图像处理中得到广泛应用，但其几乎尚未受到关注。为扩展特征来源域并增强特征表示，我们提出了一种新的三域策略（Tridos），在时空域基础上引入频率感知记忆增强，用于红外小目标检测。在该方案中，它通过一个采用傅里叶变换的局部-全局频率感知模块，有效分离并增强频率特征。受人类视觉系统启发，我们的记忆增强旨在捕捉视频帧之间红外目标的空间关系。此外，它通过差分学习和残差增强对时间动态运动特征进行编码。另外，我们进一步设计了残差补偿，以协调可能存在的跨域特征不匹配。据我们所知，所提出的 Tridos 是首个在时空频三个域中全面探索红外目标特征学习的工作。在三个数据集（即 DAUB、ITSDT-15K 和 IRDST）上进行的大量实验验证了，我们的三域红外特征学习方案通常能够明显优于最先进方法。源代码可在 https://github.com/UESTC-nnLab/Tridos 获取。
英文摘要：
As a sub-field of object detection, moving infrared small target detection presents significant challenges due to tiny target sizes and low contrast against backgrounds. Currently-existing methods primarily rely on the features extracted only from spatio-temporal domain. Frequency domain has hardly been concerned yet, although it has been widely applied in image processing. To extend feature source domains and enhance feature representation, we propose a new Triple-domain Strategy (Tridos) with the frequency-aware memory enhancement on spatio-temporal domain for infrared small target detection. In this scheme, it effectively detaches and enhances frequency features by a local-global frequency-aware module with Fourier transform. Inspired by human visual system, our memory enhancement is designed to capture the spatial relations of infrared targets among video frames. Furthermore, it encodes temporal dynamics motion features via differential learning and residual enhancing. Additionally, we further design a residual compensation to reconcile possible cross-domain feature mismatches. To our best knowledge, proposed Tridos is the first work to explore infrared target feature learning comprehensively in spatio-temporal-frequency domains. The extensive experiments on three datasets (i.e., DAUB, ITSDT-15K and IRDST) validate that our triple-domain infrared feature learning scheme could often be obviously superior to state-of-the-art ones. Source codes are available at https://github.com/UESTC-nnLab/Tridos.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
