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Deep Learning on Underwater Marine Object Detection: A Survey

机译：水下海洋物体检测的深度学习：一项调查

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摘要

Deep learning, also known as deep machine learning or deep structured learning based techniques, have recendy achieved tremendous success in digital image processing for object detection and classification. As a result, they are rapidly gaining popularity and attention from the computer vision research community. There has been a massive increase in the collection of digital imagery for the monitoring of underwater ecosystems, including seagrass meadows. This growth in image data has driven the need for automatic detection and classification using deep neural network based classifiers. This paper systematically describes the use of deep learning for underwater imagery analysis within the recent past. The analysis approaches are categorized according to the object of detection, and the features and deep learning architectures used are highlighted. It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass.
机译：深度学习（也称为深度机器学习或基于深度结构化学习的技术）在用于对象检测和分类的数字图像处理中取得了巨大的成功。结果，它们迅速地受到了计算机视觉研究界的欢迎和关注。用于监视水下生态系统（包括海草草甸）的数字图像的收集已大大增加。图像数据的增长推动了对使用基于深度神经网络的分类器进行自动检测和分类的需求。本文系统地描述了近来深度学习在水下图像分析中的应用。根据检测对象对分析方法进行了分类，并重点介绍了所使用的功能和深度学习架构。结论是，在使用深度神经网络的数字海底图像分析中，自动化的应用范围很大，尤其是对海草的检测和监视。

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