Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of ocean ecosystems, the former due to their function at many levels of the oceans food chain, the latter because they provide spawning and nursery grounds to many fish populations. Studying and monitoring plankton distribution and coral reefs is vital for environment protection. In the last years there has been a massive proliferation of digital imagery for the monitoring of underwater ecosystems and much research is concentrated on the automated recognition of plankton and corals. In this paper, we present a study about an automated system for monitoring of underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning pretrained CNN for underwater imagery analysis, the opportunity of using different datasets for pretraining models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. The experimental results are very encouraging, our experiments performed on 5 well-knowns datasets (3 plankton and 2 coral datasets) show that the fusion of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report performance of both single CNN and ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model.
翻译:海洋是地球的基本生命线: 它们提供了超过70%的氧气和超过97%的水体。 普兰克顿和珊瑚是海洋生态系统的两个最基本的组成部分, 前者是由于它们在海洋食物链许多层次上的功能, 后者是因为它们为许多鱼类提供产卵和育苗场。 研究和监测浮游生物分布和珊瑚礁对于环境保护至关重要。 在过去的几年里, 用于监测水下生态系统的数字图像大量扩散, 许多研究集中在对浮游生物和珊瑚的自动识别上。 在本文中, 我们提出一份关于监测水下生态系统的自动化系统的研究, 前者是由于它们在许多层次的海洋食物链链中发挥作用, 后者是由于它们在许多层次的海洋系食物链中发挥作用。 我们研究如何根据不同的CNN模型, 仔细调整浮游生物的分布和珊瑚礁。 我们研究的目的是试验微调微调的CNNCNN模型用于水下图像分析的可能性, 利用所有不同的CNM数据集来评估如何利用同一结构来监测水下生态系统生态系统的自动系统。 我们提出的系统基于不同深度学习方法的融合; 我们研究如何用最起码的模型来模拟的实验性实验性实验性实验性地展示我们的数据。