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10 November 2020 Multiscale deep fully convolutional network for sea-land segmentation of surveillance images
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Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158406 (2020) https://doi.org/10.1117/12.2579189
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China
Abstract
Sea-Land segmentation based on surveillance images is an important research content for real-time coast monitoring. However, the complex weather and environmental makes the segmentation of sea-land is a difficult task. Although previous deep learning methods based on convolutional neural networks have achieved excellent results in semantic segmentation, and there has been some work using deep convolutional neural networks for Sea-Land segmentation but we hope that the image segmentation model can achieve more accurate results in sea and land segmentation. In our method, we propose a novel sea-land segmentation framework called Multi Sea-Land U-net (MSLUnet), the framework base on a multi-scale. The proposed MSLUnet is mainly composed of a multi-scale layer and U-Net convolutional network. The multi-scale input layer constructs an image pyramid to accept multiple levels of image data in the network model. U-shaped convolutional networks are used as the back-bone network structure to learn rich hierarchical representations. Experimental results show that compared with other architectures, MSLUnet has achieved good performance.
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Xiaoyu Wu, Gong Chen, Xiangqian Ding, KeZhen Xie, Guanqun Wei, and Wanlin Lu "Multiscale deep fully convolutional network for sea-land segmentation of surveillance images", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158406 (10 November 2020); https://doi.org/10.1117/12.2579189
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