针对当前无人机图像时敏目标类增量检测面临的灾难性遗忘、过拟合以及难以适配密集检测器特性导致检测精度受限等问题，提出了一种基于多阶段蒸馏的时敏目标增量检测算法，算法主要包含基于连续Wasserstein距离的类间蒸馏（WICD）模块、基于原型引导的类内一致性蒸馏（PGICD）模块以及交叉预测自适应蒸馏（CAD）模块。WICD模块从特征图和语义查询向量中捕捉类间特征差异，利用高斯分布与连续Wasserstein距离，增强类间区分性。PGICD模块通过最小化教师网络和学生网络中实例的高层语义查询和低层特征图的原型差异，实现类内特征有效传递，增强类内一致性。CAD模块通过动态调整分类和回归分支的蒸馏权重，优化交叉预测蒸馏过程，缓解了增量学习中灾难性遗忘问题，提升了模型在复杂场景下的检测精度。在SIMD和MAR20数据集上的实验结果显示，所提方法在各类型的单步和多步增量场景下均表现优异，平均精度（AP）相比传统方法有显著提升。在SIMD数据集8类+7类的增量场景下，AP高达70.8%，与上限绝对差距为1.7%，相对差距为2.3%。在MAR20数据集10类+10类的增量场景下，AP高达60.2%，与上限的绝对差距为2.3%，相对差距为3.6%。此外，通过消融实验验证了各模块有效性，所得结果表明各模块有效地提升了无人机图像时敏目标增量检测性能。

To address the problems of catastrophic forgetting， overfitting， and difficulty in adapting dense detector characteristics leading to limited detection accuracy faced by the current time-sensitive target class incremental object detection of UAV images， this paper proposes a time-sensitive target incremental detection algorithm based on multi-stage distillation. The proposed method includes three key modules： the continuous Wasserstein distance-based Inter-Class Distillation （WICD） module， the Prototype-Guided Intra-class Consistency Distillation （PGICD） module， and the Cross-prediction Adaptive Distillation （CAD） module. The WICD module captures the inter-class feature differences from the feature graphs and semantic query vectors using Gaussian distributions with continuous Wasserstein distances to enhance the inter-class discriminability. The PGICD module enhances the inter-class discriminative properties by minimizing the high-level semantic query of the instances in both the teacher’s network and student’s network， and the low-level feature graphs with the prototype differences to achieve effective intra-class feature transfer and enhance intra-class consistency. The CAD module optimizes the cross-prediction distillation process by dynamically adjusting the distillation weights of the classification and regression branches， mitigating the problem of catastrophic forgetting in incremental learning， and improving the model’s detection accuracy in complex scenarios. Experimental results on SIMD and MAR20 datasets show that the proposed method performs well in all types of one-step and multi-step incremental scenarios， and the Average Precision （AP） is significantly improved compared with traditional methods. The AP is as high as 70.8% in the incremental scenario of SIMD dataset with 8+7 classes， with an absolute gap of 1.7% and a relative gap of 2.3% from the upper limit. The results also show that the AP is as high as 60.2% in the incremental scenario of MAR20 dataset with 10+10 classes， with an absolute gap of 2.3% and a relative gap of 3.6% from the upper limit. In addition， the effectiveness of each module is verified by ablation experiments， which effectively improves the incremental detection performance of time-sensitive targets in UAV images.