论文标题
Resattunet：使用注意力激活的残留UNET检测海洋碎片
ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet
论文作者
论文摘要
当前，使用深度学习技术在遥感领域进行了大量研究。引入具有基准结果的开源数据集海洋碎片档案馆（MARIDA），用于海洋碎片检测开辟了新的途径，将使用深度学习技术用于碎片检测和细分任务。本文介绍了一种新型的基于注意力的分割技术，该技术的表现优于Marida引入的现有最新结果。本文介绍了一种新颖的空间意识编码器和解码器架构，以维护图像中存在的稀疏地面真相贴片的上下文信息和结构。预计所达到的结果将为进一步的研究铺平道路，涉及使用遥感图像进行深度学习。该代码可从https://github.com/sheikhazhanmohammed/sadma.git获得
Currently, a significant amount of research has been done in field of Remote Sensing with the use of deep learning techniques. The introduction of Marine Debris Archive (MARIDA), an open-source dataset with benchmark results, for marine debris detection opened new pathways to use deep learning techniques for the task of debris detection and segmentation. This paper introduces a novel attention based segmentation technique that outperforms the existing state-of-the-art results introduced with MARIDA. The paper presents a novel spatial aware encoder and decoder architecture to maintain the contextual information and structure of sparse ground truth patches present in the images. The attained results are expected to pave the path for further research involving deep learning using remote sensing images. The code is available at https://github.com/sheikhazhanmohammed/SADMA.git
