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Rotated Region Based CNN for Ship Detection
Liu ZK(刘子坤)1,2; Hu JG(胡锦高)1,2; Weng LB(翁璐斌)1; Yang YP(杨一平)1; Weng LB(翁璐斌)
Conference Name2017 IEEE International Conference on Image Processing
Conference Date17-20 September 2017
Conference PlaceChina National Convention Center in Beijing, China
AbstractThe state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in remote sensing images. In this paper, we embrace this observation and introduce the rotated region based CNN (RR-CNN), which can learn and accurately extract features of rotated regions and locate rotated objects precisely. RR-CNN has three important new components including a rotated region of interest (RRoI) pooling layer, a rotated bounding box regression model and a multi-task method for non-maximal suppression (NMS) between different classes. Experimental results on the public ship dataset HRSC2016 confirm that RR-CNN outperforms baselines by a large margin.
KeywordRotated Region Convolutional Neural Network Ship Detection
Document Type会议论文
Corresponding AuthorWeng LB(翁璐斌)
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu ZK,Hu JG,Weng LB,et al. Rotated Region Based CNN for Ship Detection[C],2017.
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