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Critical object recognition in underwater environment

机译：水下环境中的关键对象识别

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Nowadays, ocean exploration is far from complete and the development of suitable recognition systems are crucial, to allow that the robots perform inspection and monitoring tasks in diverse conditions. The online available datasets are incomplete for these kinds of scenarios and, so it is important to build datasets that covered real condition in a simulated environment. Thus, it was developed a dataset with some man-made objects presents in the underwater environment. Moreover, it is also presented the developed method (Convolutional Neural Network) and its evaluation in diverse conditions is performed. It is also presented a comparative analysis and a discussion between the proposed algorithm and the ResNet architecture. The obtained results showed that the developed method is appropriate to classify 7 critical different objects with good performance.
机译：如今，海洋探索远非完整，合适的识别系统的发展至关重要，以便机器人在不同条件下进行检查和监控任务。在线可用数据集对于这些方案不完整，因此构建在模拟环境中涵盖真实情况的数据集是重要的。因此，它开发了一个具有一些人造物体的数据集，在水下环境中存在。此外，还介绍了开发的方法（卷积神经网络），并进行了各种情况的评估。它还介绍了所提出的算法和Reset架构之间的比较分析和讨论。所获得的结果表明，开发方法适合于将7个临界不同对象进行分类，具有良好的性能。

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