Traditional data-driven algorithms suffer from data reliance, hyperparameter sensitivity, and faint characteristics in infrared (IR) “low, slow, and small” unmanned aerial vehicle target detection and recognition, resulting in performance degradation in complex backgrounds. Inspired by model-driven methods, this article proposes a learnable feature modulation module that uses prior knowledge to enhance feature representation. Specifically, this method converts the local contrast measure into a nonlocal quadrature difference measure in deep feature space, considering feature points that break semantic continuity as the potential target locations through a self-attentive approach. On this basis, considering the scale changes of aircraft targets during radial approach to IR detectors, a multiscale single-stage detector is designed by effective receptive field calculation. In this network structure, a bidirectional serial feature modulation method is used to fully retain the multiscale features of the target and ensure adaptability to point, spot, and area targets while satisfying real-time requirements. The ablation studies verify the effectiveness of each component and help determine the optimal parameter configuration. Finally, comparison experiments with state-of-the-art methods are conducted on a 10k scale IR dataset. The experimental results show that the detection accuracy of this method is better than that of other baseline methods while ensuring real-time performance, especially in highly complex and low-contrast scenes, achieving superior higher target detection accuracy.

中文翻译：

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复杂背景下红外旋翼无人机目标检测与识别的非局部正交对比学习


传统的数据驱动算法在红外“低、慢、小”无人机目标检测和识别中存在数据依赖、超参数敏感性和微弱特性等问题，导致复杂背景下的性能下降。受模型驱动方法的启发，本文提出了一种可学习的特征调制模块，该模块使用先验知识来增强特征表示。具体来说，该方法将局部对比度度量转换为深层特征空间中的非局部正交差度量，通过自注意力方法将破坏语义连续性的特征点视为潜在的目标位置。在此基础上，考虑飞行器目标径向接近红外探测器过程中的尺度变化，通过有效感受野计算设计了多尺度单级探测器。该网络结构中采用双向串行特征调制方法，充分保留目标的多尺度特征，在满足实时性要求的同时，保证对点、点、面目标的适应性。消融研究验证每个组件的有效性并帮助确定最佳参数配置。最后，在 10k 规模的 IR 数据集上进行了与最先进方法的比较实验。实验结果表明，该方法在保证实时性的同时，检测精度优于其他基线方法，尤其是在高度复杂、低对比度的场景下，实现了更高的目标检测精度。