Revisiting Faster R CNN: A Deeper Look at Region Proposal Network
Currently, state of the art object detectors are based on Faster R CNN. We firstly revisit Faster R CNN and explore problems in it, e.g. coarseness of feature maps for accurate localization, fixed window feature extraction in RPN and insensitivity for small scale objects. Then a novel object detection network is proposed to address these problems. Specifically, we utilize a two stage cascade multi scale proposal generation network to get high accurate proposals: an original RPN is adopted to initially generate coarse proposals, then another network with multi layer features and RoI pooling layer are introduced to refine these proposals. We also generate small scale proposals in the second stage simultaneously. After that, a detection network with multi layer features further classifies and refines proposals. A novel 3 step joint training algorithm is introduced to optimize our model. Experiments on PASCAL VOC 2007 and 2012 demonstrate the effectiveness of our network.
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