Instance Segmentation of Herring and Salmon Schools in Acoustic Echograms using a Hybrid U-Net

Alex L. Slonimer, Melissa Cote, T. Marques, A. Rezvanifar, S. Dosso, A. Albu, Kaan Ersahin, T. Mudge, S. Gauthier
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引用次数: 1

Abstract

The automated classification of fish, such as herring and salmon, in multi-frequency echograms is important for ecosystems monitoring. This paper implements a novel approach to instance segmentation: a hybrid of deep-learning and heuristic methods. This approach implements semantic segmentation by a U-Net to detect fish, which are converted to instances of fish-schools derived from candidate components within a defined linking distance. In addition to four frequency channels of echogram data (67.5, 125, 200, 455 kHz), two simulated channels (water depth and solar elevation angle) are included to encode spatial and temporal information, which leads to substantial improvement in model performance. The model is shown to out-perform recent experiments that have used a Mask R-CNN architecture. This approach demonstrates the ability to classify sparsely distributed objects in a way that is not possible with state-of-the-art instance segmentation methods.
基于混合U-Net的声回波图中鲱鱼和鲑鱼种群的实例分割
在多频回波图中对鱼类(如鲱鱼和鲑鱼)进行自动分类对生态系统监测具有重要意义。本文实现了一种新的实例分割方法:深度学习和启发式方法的混合。该方法通过U-Net实现语义分割来检测鱼,并将其转换为在定义的连接距离内从候选组件派生的鱼群实例。在回波图数据的4个频率通道(67.5、125、200、455 kHz)之外,还包括水深和太阳俯仰角两个模拟通道来编码时空信息,从而大大提高了模型的性能。该模型的性能优于最近使用Mask R-CNN架构的实验。这种方法展示了以最先进的实例分割方法无法实现的方式对稀疏分布对象进行分类的能力。
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