Today’s deep learning architectures, if trained with proper dataset, can be used for object detection in marine search and rescue operations. In this paper a dataset for maritime search and rescue purposes is proposed. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect. The second contribution is our proposed object detection method. It is an ensemble composed of a number of the deep convolutional neural networks, orchestrated by the fusion module with the nonlinearly optimized voting weights. The method achieves over 82% of average precision on the new aerial-drone floating objects dataset and outperforms each of the state-of-the-art deep neural networks, such as YOLOv3, -v4, Faster R-CNN, RetinaNet, and SSD300. The dataset is publicly available from the Internet.

中文翻译：

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一种优化权重的集成深度学习方法，用于基于无人机的水救援和监控

如果使用适当的数据集进行训练，则当今的深度学习架构可用于海上搜索和救援行动中的目标检测。本文提出了用于海上搜索和救援目的的数据集。它包含有40,000名带有人工注释的人和漂浮在水中的物体的空中无人机视频，其中许多体积很小，很难被发现。第二个贡献是我们提出的目标检测方法。它是由多个深度卷积神经网络组成的合奏，由融合模块采用非线性优化的投票权重进行编排。该方法在新的空中无人机漂浮物体数据集上实现了82％以上的平均精度，并且胜过了每个最新的深度神经网络，例如YOLOv3，-v4，Faster R-CNN，RetinaNet和SSD300 。