  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.
