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Automatic Fish Segmentation on Vertical Slot Fishways Using SOM Neural Networks

机译:使用SOM神经网络在垂直槽鱼道上进行自动鱼分割

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Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The appropriate design of these should consider the behavior and biological variables of the target fish species and currently existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper studies the application of Artificial Neural Networks to the problem of automatic fish segmentation in vertical slot fishways. In particular, SOM Neural Networks have been used to detect fishes using visual information sampled by an underwater camera system. A ground true dataset was designed with experts and different approaches were tested providing promising results.
机译:垂直槽式鱼道是水力结构,可以使鱼类通过河流中的障碍物向上游迁移。这些工具的适当设计应考虑目标鱼类的行为和生物学变量,以及在这些测定法中测量鱼类行为的现有机制(例如直接观察或在标本上放置传感器)是不切实际或不适当地影响鱼类行为的。动物的行为。本文研究了人工神经网络在垂直缝隙鱼道中自动鱼段分割中的应用。特别是,SOM神经网络已被用于通过水下摄像系统采样的视觉信息来检测鱼类。与专家一起设计了一个真实的基础数据集,并测试了不同的方法,从而提供了可喜的结果。

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