无人机
计算机科学
深度学习
人工智能
卷积神经网络
目标检测
对象(语法)
搜救
集成学习
计算机视觉
模式识别(心理学)
机器学习
机器人
遗传学
生物
作者
Jan Ga̧sienica-Józkowy,Mateusz Knapik,Bogusław Cyganek
出处
期刊:Integrated Computer-aided Engineering
[IOS Press]
日期:2021-01-26
卷期号:28 (3): 221-235
被引量:58
摘要
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.
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