Selective region enlargement network for fast object detection in high resolution images

Abstract

Detecting objects of varied sizes in high resolution images is difficult due to the challenges of high memory requirement and huge computation burden. Existing state-of-the-art detectors perform well on low resolution images. However, its performance is greatly limited on high resolution images. In this paper, we propose a selective region enlargement network, called SRENet, which significantly reduces processing time and memory requirement while remaining high detection accuracy. The proposed SRENet does not need to conduct detection on original high resolution images but only needs to conduct detection on down-sampled images and some zoom-in regions selected from high resolution images. SRENet first conducts coarse detection on a low resolution image, and then sequentially selects promising regions that are expected to be analyzed at a higher resolution. Specifically, SRENet is built upon Deep Q-learning Network (DQN) and it outputs an action-reward map. The value of the reward map indicates the possibility that the action can improve detection accuracy. The region selected by the action with the maximum reward value will be analyzed further at a higher resolution. Extensive experiments are conducted to demonstrate SRENet on two challenging datasets obtaining high resolution images. Experimental results show that SRENet achieves state-of-the-art detection performance with high efficiency.