To improve the accuracy and efficiency of detecting occluded areas in large tilt images, this paper improves the Occlusion Detection Algorithm Based on Elevation Constraints on the basis of Angle Based Occlusion Detection Algorithm, and proposes an Occlusion Detection Algorithm Based on Elevation and Angle Constraints. This algorithm uses the spiral scanning method to judge the visibility of ground points layer by layer from the underground point, and fully utilizes the elevation, elevation angle with projection center, and visibility information of the inner layer point to judge the current point, greatly reducing the number of points that need to be judged. Compared with existing classical algorithms, it has the advantages of high accuracy and fast visibility analysis speed.
For the end-to-end Faster R-CNN rotation region detection algorithm, there are problems such as high difficulty in detecting frame coordinate regression and unstable structure. Compared with the detection frame, the pixel mask describes the target area at the pixel level, which is a more accurate annotation method to locate the target, and is often used in semantic segmentation.We further proposed a ship rotation region detection algorithm based on semantic segmentation. We first used an improved Focal Loss function for training the improved U-Net segmentation model integrating the space and channel attention mechanism and feature pyramid network to obtain the ship semantic segmentation mask, and then used the watershed segmentation algorithm to distinguish different instances and did the minimum outer rectangle detection to position ships rotation region after acquiring their area masks. Our method can not only accurately position the ships but also effectively obtain the scale information of them which giving an indication of certain practical value.
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