论文标题
船舶检测：参数服务器变体
Ship Detection: Parameter Server Variant
论文作者
论文摘要
卫星光学图像中的深度学习船检测遭受云，地质和人造物体的假阳性事件，这些物体会干扰正确的船舶分类，通常将类准确度得分限制在88％\％。这项工作探讨了基于云的解决方案中的自定义策略，班级准确率，培训时间和成本之间的紧张关系。我们演示了自定义U-NET如何在验证数据集上实现92 \％类的准确性，而目标数据集则具有90 \％置信度的目标数据集。我们还将单个节点体系结构与参数服务器变体进行比较，其工人充当提升机制。与最佳的单节点方法相比，目标数据集上的参数服务器变体优于目标数据集上的类精度。在经验发现的支持下，讨论了对单节点和参数服务器变体体系结构的系统性能的比较研究。
Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correct classification of ships, typically limiting class accuracy scores to 88\%. This work explores the tensions between customization strategies, class accuracy rates, training times, and costs in cloud based solutions. We demonstrate how a custom U-Net can achieve 92\% class accuracy over a validation dataset and 68\% over a target dataset with 90\% confidence. We also compare a single node architecture with a parameter server variant whose workers act as a boosting mechanism. The parameter server variant outperforms class accuracy on the target dataset reaching 73\% class accuracy compared to the best single node approach. A comparative investigation on the systematic performance of the single node and parameter server variant architectures is discussed with support from empirical findings.
