顺序热红外反无人机检测中的灾难性遗忘：尺度条件梯度不平衡的作用

中文摘要 AI 辅助

基于热红外探测的反无人机系统必须在运行数据集演变时保持准确性，然而顺序微调会导致对先前任务的灾难性遗忘，这一问题在该领域尚未得到充分表征。这项持续学习研究衡量了YOLOMG中的稳定性-可塑性权衡，YOLOMG是一个基于YOLOv5的检测器，以单热红外流运行并禁用运动通道，在三个难度尺度递增的反无人机基准上顺序训练：Anti-UAV-RGBT、Anti-UAV410和CST Anti-UAV。在CST上的朴素微调相对于第一阶段上限产生-0.605的遗忘度量，对应90%的能力损失，其中-0.572单独发生在第三阶段。相比之下，从冻结教师模型进行知识蒸馏在三个随机种子上与FM = -0.033 +/- 0.004相关，对应95%的保留率。由于未包含第二阶段无知识蒸馏的对照组，该结果确立了在知识蒸馏训练下的保留率，而非知识蒸馏的因果效应。按分层分析显示，大目标检测在第一个周期内崩溃至接近零，尽管阶段间梯度更新权重的余弦相似度为0.987，这表明尺度条件梯度不平衡（而非权重漂移）是候选机制。尺度分层抽样（SSH），一个在四个无人机尺寸层间平衡的300样本缓冲区，大致将遗忘减半（FM从-0.605降至-0.311），并保持大目标检测非零。消融实验将该增益主要归因于尺度分层而非抽样：随机分层回放表现至少同样好（FM = -0.221，对比SSH的-0.311）。这些回放结果为单一种子，因此应视为初步结果。

英文摘要

Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastrophic forgetting of prior tasks, a problem that remains insufficiently characterized in this domain. This continual-learning study measures the stability-plasticity trade-off in YOLOMG, a YOLOv5-based detector run as a single thermal-infrared stream with the motion channel disabled, trained sequentially across three anti-UAV benchmarks of rising scale difficulty: Anti-UAV-RGBT, Anti-UAV410, and CST Anti-UAV. Naive fine-tuning on CST yields a Forgetting Measure of -0.605 against the Stage 1 ceiling, corresponding to a 90% capability loss, with -0.572 occurring in Stage 3 alone. In contrast, knowledge distillation from a frozen teacher is associated with FM = -0.033 +/- 0.004 across three seeds, corresponding to 95% retention. Because no Stage 2 no-KD control is included, this result establishes retention under KD training rather than a causal KD effect. Per-stratum analysis shows large-target detection collapsing to near zero within the first epoch, despite an inter-stage cosine similarity of 0.987 over the gradient-updated weights, pointing to scale-conditioned gradient imbalance, rather than weight drift, as a candidate mechanism. Scale-Stratified Herding (SSH), a 300-exemplar buffer balanced across four UAV size strata, roughly halves the forgetting (FM = -0.605 to -0.311) and keeps large-target detection non-zero. An ablation attributes the gain primarily to scale stratification rather than herding: random-stratified replay performs at least as well (FM = -0.221 versus -0.311 for SSH). These replay results are single-seed and should therefore be treated as preliminary.

发表机构

• University of Amsterdam（阿姆斯特丹大学）
• SUNY Empire State University（纽约州立大学帝国州立学院）

机构由 AI 辅助整理，请以论文原文为准。