State-of-the-art object detection networks have reduced the running time and get better detection results. However, for remote sensing image scenes, in a certain projection direction, the remote sensing image target will be tilted, The positioning of the horizontal bounding box used by the exit detection algorithm will cause a lot of overlap between the target bounding boxes, After using NMS (Non-maximum suppression),it will lead to the lost of the target. In this paper,we propose a Rotated Faster R-CNN(R-FRCNN) that is a target positioning method based on arbitrary angle bounding box,which can perform non-redundant positioning on the target,thus can reduce the missed detection rate. when the target is densely distributed or the angle of the object is arbitrary.Compared with traditional and state-ofthe art object detection algorithms,our approach obtain the superior performance.
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