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Shangwen Sun

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Proceedings Article | 27 March 2024 Paper

Target rotation region detection based on semantic segmentation

Yu Liu, Dejun Li, Zhaohong Xu, Shangwen Sun

Proceedings Volume 13105, 1310512 (2024) https://doi.org/10.1117/12.3026607

KEYWORDS: Image segmentation, Target detection, Classification systems, Object detection, Detection and tracking algorithms, Remote sensing, Feature extraction

Read Abstract +

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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