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

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 $50\%$.

• Machine Perception

• Robotics