Object detection is one of the research hotspots in remote sensing image analysis and processing,and has very important research and application value.Traditional remote sensing image target detection method uses the shallow artificial feature,and combines with classifiers,such as support vector machine,random forest and Adaboost,to realize object detection task.In recent years,deep learning,especially convolution neural network (CNN),has achieved great success in image recognition.In the field of target detection,the Faster R-CNN algorithm has made a breakthrough,the detection accuracy has been greatly improved,and the detection procedure has achieved nearly real-time performance.However,the Faster R-CNN algorithm uses RoI (region of interests) pooling layer,the RoI-wise calculation is not shared,so the detection speed is still unsatisfying.R-FCN,however,is based on the fully convolution network structure,and the position-sensitive pool is used to tackle the translation variance problem,which is concealed by the convolution neural network,thus lead to performance improvement.In this paper,the principle of R-FCN is described and applied to the detection of high-resolution remote sensing images.Different parameters and network structure are analyzed to find the best configuration.The performance of three popular algorithms,including Fast R-CNN,Faster R-CNN and R-FCN are compared.Experimental results show that R-FCN can achieve 99.3% precision and achieve at a test-time speed of 180 ms per image.
