Monitoring coastal environments using imaging sonars and machine learning

成像声纳现在能够在水下海洋环境中以帧速率(通常为8-30f/ps)产生类似视频的图像。这种系统在浑浊的海岸和河口环境中工作良好，在这些环境中，弱光视频系统不能提供有用的图像，因此成像声纳提供了新的遥感工具，用于研究以前对工业和海洋管理人员重要的棘手问题，包括探测发电站取水口的潜在堵塞生物和沿海结构周围的鱼类行为。然而，这种系统可以产生的数据量(TB/天)对其常规部署造成了真正的障碍，因为工作人员需要分析图像以及相关的费用和延误。最近，机器视觉模块在能力方面取得了进展，使其成为水下遥感系统的实际组成部分，这些系统通常严重限制了通往陆上数据处理地点的通信链路的可用功率和带宽。该项目旨在开发自动机器学习来近实时地检测和分类感兴趣的目标，从而极大地降低图像分析成本，并打开此类系统在自主遥感应用中的使用。用于检测和分类成像声纳特征的传统图像处理技术使用基于像素的监督分类。然而，在有大量数据可用的情况下，这些方法是无效的--增加了成本并导致数据生产的延迟。此外，尽管数据量很大，但声纳镜头可能在很长一段时间内很少包含相关物体的出现。这就是说，虽然成像声纳镜头捕获和稍后注释的成本很高，但鱼、水母等对象的外观通常与使用传统RGB相机获取的外观相似。因此，研究将集中于开发机器学习算法，通过让声纳图像(目标域)也学习其他成像域(源域)，例如传统的RGB来辅助处理声纳图像(目标域)。开发的算法将属于深度学习算法家族，这是一种复杂的机器学习技术，最近被证明在许多计算机视觉应用中提供了一种转变。这将需要大量用于培训的带注释的图像数据集，以及CEFAS提供的关于图像外观的专业知识。随着研究的进展，该学生还将为CEFAS声纳的部署做出贡献。NEXUSS CDT为环境科学提供尖端的智能和自主观测系统的应用和开发方面的最先进、高度经验的培训，以及全面的个人和专业发展。通过与学术、研究和产业/政府/政策合作伙伴的广泛网络互动，学生将有广泛的机会扩展他们的多学科视野。这名学生将注册在东英吉利大学，由图形、视觉和语音实验室的计算科学学院主办。学生将接受与该项目相关的所有领域的培训，包括计算机视觉、机器学习以及MatLab和Python编程。学生将在Cefas、Lowestoft和南安普顿大学度过一段时间，以熟悉该项目的图像和生态方面。

Imaging sonars are now capable of producing video like images at frame rates (typically 8-30 f/ps) in the underwater marine environment. Such systems work well in the turbid coastal and estuarine environments where low light video systems do not provide useful imagery and as such imaging sonars provide new remote sensing tools for studying previously intractable problems of importance to industry and to marine managers including detection of potential clogging organisms for power station water intakes and fish behaviour around coastal structures.However, the amount of data that such systems can generate (Tb/day) creates a real barrier to their routine deployment due to the staff requirement to analyse images and the associated costs and delays. Recently, there have been advances in the capability of machine vision modules making them now practical components of underwater remote sensing systems which typically have severe constraints on the available power and bandwidth of communications links to data processing locations onshore. This project aims to develop automated machine learning to detect and classify targets of interest in near real time thereby dramatically reducing the image analysis costs and opening up the use of such systems in autonomous remote sensing applications.Traditional image processing techniques employed to detect and classify imaging sonar features use pixel-based supervised classification. However, these are ineffective in scenarios where large quantities of data are available - increasing costs and causing delays in data production. Moreover, despite the large volume of the data, the sonar footage may contain few occurrences of relevant objects for long periods of time. This said, while the imaging sonar footage is expensive to capture and later annotate, the appearance of objects e.g. fish, jelly fish etc. often bears resemblance to that acquired using the traditional RGB cameras. Consequently, the research will concentrate on developing machine learning algorithms capable of aiding processing of the sonar images (target domain) by letting them learn other imaging domains (source domains) e.g. traditional RGB as well. The developed algorithms will belong to the family of 'deep learning' algorithms, a complex machine learning technique that has recently proven to provide a step-change in a number of computer vision applications. This will require a large dataset of annotated imagery for training and the expert knowledge on the image appearance which are available in Cefas. The student will also contribute to the deployment of Cefas sonars as the research progresses. The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of East Anglia, hosted at School of Computing Sciences in the Graphics, Vision and Speech laboratory. The student will receive training in all areas relevant to the project including computer vision, machine learning as well as Matlab and Python programming. The student will spend periods of time at Cefas, Lowestoft and University of Southampton in order to familiarize with the images and the ecological aspects of the project.