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Dynamic Zoom-in Network for Fast Object Detection in Large Images

Mingfei Gao
Ruichi Yu
Ang Li
Vlad I. Morariu
Larry S. Davis
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018
Google Scholar


We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images. Detection progresses in a coarse-to-fine manner, first on a down-sampled version of the image and then on a sequence of higher resolution regions identified as likely to improve the detection accuracy. Built upon reinforcement learning, our approach consists of a model (R-net) that uses coarse detection results to predict the potential accuracy gain for analyzing a region at a higher resolution and another model (Q-net) that sequentially selects regions to zoom in. Experiments on the Caltech Pedestrians dataset show that our approach reduces the number of processed pixels by over $50\%$ without a drop in detection accuracy. The merits of our approach become more significant on a high resolution test set collected from YFCC100M dataset where our approach maintains high detection performance while reducing the number of processed pixels by about $70\%$ and the detection time by over $50\%$.