The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher
detection speed while keeping the detection performance, the global structure information is ignored by the positionsensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the coupling module which
consists of two branches. One branch adopts the positionsensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments
demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e.
a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4%
on COCO. Codes will be made publicly available.