This paper investigates the problem of class-incremental object detection for agricultural applications where a model needs to learn new plant species and diseases incrementally without forgetting the previously learned ones. We adapt two public datasets to include new categories over time, simulating a more realistic and dynamic scenario. We then compare three class-incremental learning methods that leverage different forms of knowledge distillation to mitigate catastrophic forgetting. Our experiments show that all three methods suffer from catastrophic forgetting, but the Dynamic Y-KD approach, which additionally uses a dynamic architecture that grows new branches to learn new tasks, outperforms ILOD and Faster-ILOD in most settings both on new and old classes. These results highlight the challenges and opportunities of continual object detection for agricultural applications. In particular, we hypothesize that the large intra-class and small inter-class variability that is typical of plant images exacerbate the difficulty of learning new categories without interfering with previous knowledge. We publicly release our code to encourage future work.

翻译：本文研究了农业应用中类增量目标检测问题，即模型需在不遗忘先前所学知识的前提下，逐步学习新的植物物种与病害。我们将两个公开数据集调整为随时间动态引入新类别，以模拟更真实且动态的应用场景。随后，我们比较了三种利用不同形式知识蒸馏来缓解灾难性遗忘的类增量学习方法。实验表明，三种方法均存在灾难性遗忘现象，但动态Y-KD方法（该方法采用动态架构，通过扩展新分支学习新任务）在多数新旧类别场景中均优于ILOD与Faster-ILOD。这些结果凸显了农业应用中持续目标检测面临的挑战与机遇。特别地，我们推测植物图像典型的类内差异大、类间差异小的特性，加剧了在不干扰已有知识的前提下学习新类别的难度。我们已公开代码以鼓励后续研究。