Learning Nonlocal Quadrature Contrast for Detection and Recognition of Infrared Rotary-Wing UAV Targets in Complex Background

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.