Paper
27 March 2024 Target rotation region detection based on semantic segmentation
Author Affiliations +
Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310512 (2024) https://doi.org/10.1117/12.3026607
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yu Liu, Dejun Li, Zhaohong Xu, and Shangwen Sun "Target rotation region detection based on semantic segmentation", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310512 (27 March 2024); https://doi.org/10.1117/12.3026607
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KEYWORDS
Image segmentation

Target detection

Classification systems

Object detection

Detection and tracking algorithms

Feature extraction

Remote sensing

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