Integrated Knowledge Distillation for Efficient One-stage Object Detection Network

Abstract

Due to the low latency requirements in object detection, numbers of one-stage methods like YOLO and SSD adopt a shared head for both classification and localisation tasks. While the decoupled head used to decouple the subtasks into different heads are getting more popular in one-stage detection because they improve accuracy. In contrast, the computational complexity caused by the decoupled head cant be ignored. To solve these problems, we propose an integrated knowledge distillation framework for transferring the representation ability of the decoupled head to the original coupled head and contributing to efficient one-stage object detection. It solves the problem that the coupled head is insufficient in handling the conflict of subtasks and avoids the time delay introduced by the coupling head and the increase of network parameters.