The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep learning networks are ill-equipped for incremental learning. When a well-trained network is adapted to new categories, its performance on the old categories will dramatically degrade. To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models. However, the state-of-the-art incremental object detector employs an external fixed region proposal method that increases overall computation time and reduces accuracy comparing to Region Proposal Network (RPN) based object detectors such as Faster RCNN. The purpose of this paper is to design an efficient end-to-end incremental object detector using knowledge distillation. We first evaluate and analyze the performance of the RPN-based detector with classic distillation on incremental detection tasks. Then, we introduce multi-network adaptive distillation that properly retains knowledge from the old categories when fine-tuning the model for new task. Experiments on the benchmark datasets, PASCAL VOC and COCO, demonstrate that the proposed incremental detector based on Faster RCNN is more accurate as well as being 13 times faster than the baseline detector.

人类的视觉和感知系统本质上是增量的，其中随着时间的推移不断学习新知识，同时保留现有知识。另一方面，深度学习网络不具备进行增量学习的能力。当训练有素的网络适应新类别时，其在旧类别上的性能将大大降低。为了解决这个问题，已经探索了增量学习方法，该方法保留了深度学习模型的旧知识。但是，与基于区域提议网络（RPN）的对象检测器（如Faster RCNN）相比，最新的增量对象检测器采用了外部固定区域建议方法，该方法增加了总体计算时间并降低了准确性。本文的目的是利用知识蒸馏设计一种高效的端到端增量物体检测器。我们首先通过对增量检测任务进行经典蒸馏来评估和分析基于RPN的检测器的性能。然后，我们引入了多网络自适应精馏，当针对新任务微调模型时，该方法会适当保留旧类别中的知识。在基准数据集PASCAL VOC和COCO上进行的实验表明，基于Faster RCNN提出的增量式检测器更准确，并且比基线检测器快13倍。我们引入了多网络自适应精馏，可以在针对新任务微调模型时适当保留旧类别中的知识。在基准数据集PASCAL VOC和COCO上进行的实验表明，基于Faster RCNN提出的增量式检测器更准确，并且比基线检测器快13倍。我们引入了多网络自适应精馏，可以在针对新任务微调模型时适当保留旧类别中的知识。在基准数据集PASCAL VOC和COCO上进行的实验表明，基于Faster RCNN提出的增量式检测器更准确，并且比基线检测器快13倍。