A Multi-strategy Region Proposal Network

In the experiment, MSRPN is compared with the Fast Region-based Convolutional Network (Fast R-CNN), Faster R-CNN, Inside-Outside Net (ION), Multi-region CNN (MR-CNN) and HyperNet approaches. MSRPN achieves the state-of-the-art mean average precision (mAP) of 78.9%, 74.8% and 32.1% on PASCAL VOC 2007, 2012 and MS COCO data sets with the deep VGG-16 model, surpassing other five object detection methods. Simultaneously, the above experiment results are obtained by MSRPN with only 150 region proposals per image. Additionally, MSRPN gets excellent performance on small object detection. Furthermore, MSRPN runs at 6 fps which is faster than other methods. In conclusion, the MSRPN method can provide important support for the intelligent object detection systems.