Autonomous agents such as indoor drones must learn new object classes in real-time while limiting catastrophic forgetting, motivating Class-Incremental Learning (CIL). However, most unmanned aerial vehicle (UAV) datasets focus on outdoor scenes and offer limited temporally coherent indoor videos. We introduce an indoor dataset of $14,400$ frames capturing inter-drone and ground vehicle footage, annotated via a semi-automatic workflow with a $98.6\%$ first-pass labeling agreement before final manual verification. Using this dataset, we benchmark 3 replay-based CIL strategies: Experience Replay (ER), Maximally Interfered Retrieval (MIR), and Forgetting-Aware Replay (FAR), using YOLOv11-nano as a resource-efficient detector for deployment-constrained UAV platforms. Under tight memory budgets ($5-10\%$ replay), FAR performs better than the rest, achieving an average accuracy (ACC, $mAP_{50-95}$ across increments) of $82.96\%$ with $5\%$ replay. Gradient-weighted class activation mapping (Grad-CAM) analysis shows attention shifts across classes in mixed scenes, which is associated with reduced localization quality for drones. The experiments further demonstrate that replay-based continual learning can be effectively applied to edge aerial systems. Overall, this work contributes an indoor UAV video dataset with preserved temporal coherence and an evaluation of replay-based CIL under limited replay budgets. Project page: https://spacetime-vision-robotics-laboratory.github.io/learning-on-the-fly-cl

翻译：室内无人机等自主智能体必须在实时学习新物体类别的同时限制灾难性遗忘，这推动了类增量学习（CIL）的研究。然而，大多数无人机（UAV）数据集聚焦于室外场景，且提供的具有时间连贯性的室内视频有限。我们引入了一个包含 $14,400$ 帧的室内数据集，其中捕捉了无人机间及地面车辆的影像，并通过一个半自动标注流程进行标注，在最终人工验证前首次标注一致性达到 $98.6\%$。利用该数据集，我们以 YOLOv11-nano 作为资源受限无人机平台部署的高效检测器，对三种基于回放的 CIL 策略进行了基准测试：经验回放（ER）、最大干扰检索（MIR）和遗忘感知回放（FAR）。在严格的内存预算下（$5-10\%$ 回放），FAR 表现优于其他方法，在 $5\%$ 回放率下实现了 $82.96\%$ 的平均准确率（ACC，即跨增量步骤的 $mAP_{50-95}$）。基于梯度加权的类激活映射（Grad-CAM）分析显示了在混合场景中注意力在不同类别间的转移，这与无人机定位质量的下降相关。实验进一步证明，基于回放的持续学习可以有效地应用于边缘空中系统。总体而言，本工作贡献了一个具有时间连贯性的室内无人机视频数据集，并在有限回放预算下评估了基于回放的 CIL 方法。项目页面：https://spacetime-vision-robotics-laboratory.github.io/learning-on-the-fly-cl