Multi-scale infrared unmanned aerial vehicle ( UAV) targets (IRUTs) detection under dynamic scenarios remains a challenging task due to weak target features, varying shapes and poses, and complex background interference. Current detection methods find it difficult to address the above issues accurately and efficiently. In this paper, we design a dynamic attentive network (DANet) incorporating a scale-adaptive feature enhancement mechanism (SaFEM) and an attention-guided cross-weighting feature aggregator (ACFA). The SaFEM adaptively adjusts the network's receptive fields at hierarchical network levels leveraging separable deformable convolution (SDC), which enhances the network's multi-scale IRUT awareness. The ACFA, modulated by two crossing attention mechanisms, strengthens structural and semantic properties on neighboring levels for the accurate representation of multi-scale IRUT features from different levels. A plug-and-play anti-distractor contrastive regularization (ADCR) is also imposed on our DANet, which enforces similarity on features of targets and distractors from a new uncompressed feature projector (UFP) to increase the network's anti-distractor ability in complex backgrounds. To further increase the multi-scale UAV detection performance of DANet while maintaining its efficiency superiority, we propose a novel scale-specific knowledge distiller (SSKD) based on a divide-and-conquer strategy. For the "divide" stage, we intendedly construct three task-oriented teachers to learn tailored knowledge for small-, medium-, and largescale IRUTs. For the "conquer" stage, we propose a novel elementwise attentive distillation module (EADM), where we employ a pixel-wise attention mechanism to highlight teacher and student IRUT features, and incorporate IRUT-associated prior knowledge for the collaborative transfer of refined multi-scale IRUT features to our DANet. Extensive experiments on real infrared UAV datasets demonstrate that our DANet is able to detect multi-scale UAVs with a satisfactory balance between accuracy and efficiency.
