  Object detection models shipped with camera-equipped edge devices cannot
cover the objects of interest for every user. Therefore, the incremental
learning capability is a critical feature for a robust and personalized object
detection system that many applications would rely on. In this paper, we
present an efficient yet practical system, RILOD, to incrementally train an
existing object detection model such that it can detect new object classes
without losing its capability to detect old classes. The key component of RILOD
is a novel incremental learning algorithm that trains end-to-end for one-stage
deep object detection models only using training data of new object classes.
Specifically to avoid catastrophic forgetting, the algorithm distills three
types of knowledge from the old model to mimic the old model's behavior on
object classification, bounding box regression and feature extraction. In
addition, since the training data for the new classes may not be available, a
real-time dataset construction pipeline is designed to collect training images
on-the-fly and automatically label the images with both category and bounding
box annotations. We have implemented RILOD under both edge-cloud and edge-only
setups. Experiment results show that the proposed system can learn to detect a
new object class in just a few minutes, including both dataset construction and
model training. In comparison, traditional fine-tuning based method may take a
few hours for training, and in most cases would also need a tedious and costly
manual dataset labeling step.
