Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms
Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms
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DOI：
10.1109/jsen.2020.3032031
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发表时间：
2021-02-15
影响因子：
4.3
通讯作者：
Mackiewicz, Michal
中科院分区：
文献类型：
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作者：
Gorpincenko, Artjoms;French, Geoffrey;Mackiewicz, Michal
人类企业经常遭受水母大量繁殖造成的直接负面影响。对先前水母监测系统的调查表明，它无法在交叉验证设置中可靠地执行，即在新的水下环境中。在本文中，对系统中负责对象分类的部分提出了一些改进。首先，通过添加合成数据来增强训练集，使深度学习分类器能够更好地泛化。然后，通过使用新的第二阶段模型对框架进行增强，该模型分析第一个网络的输出以进行最终预测。最后，加入加权损失和置信阈值来平衡真阳性和假阳性。通过所有升级，系统可以正确分类30.16%（与初始的11.52%相比）的斑点水母，将误报率保持在0.91%（与初始的2.26%相比），并在自主嵌入式平台的计算约束下实时运行。
Human enterprise often suffers from direct negative effects caused by jellyfish blooms. The investigation of a prior jellyfish monitoring system showed that it was unable to reliably perform in a cross validation setting, i.e. in new underwater environments. In this paper, a number of enhancements are proposed to the part of the system that is responsible for object classification. First, the training set is augmented by adding synthetic data, making the deep learning classifier able to generalise better. Then, the framework is enhanced by employing a new second stage model, which analyzes the outputs of the first network to make the final prediction. Finally, weighted loss and confidence threshold are added to balance out true and false positives. With all the upgrades in place, the system can correctly classify 30.16% (comparing to the initial 11.52%) of all spotted jellyfish, keep the amount of false positives as low as 0.91% (comparing to the initial 2.26%) and operate in real-time within the computational constraints of an autonomous embedded platform.