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8-30 f/ps）产生类似视频的图像。这样的系统在混浊的沿海和河口环境中工作良好，其中低光视频系统不提供有用的图像，并且这样的成像声纳提供了新的遥感工具，用于研究对工业和海洋管理者重要的以前难以解决的问题，包括检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。由于分析图像的工作人员需求以及相关的成本和延迟，这种系统可以生成的数据量（TB/天）对其日常部署造成了真实的障碍。最近，机器视觉模块的能力已经取得了进展，使它们现在成为水下遥感系统的实用组件，这些水下遥感系统通常对与岸上数据处理位置的通信链路的可用功率和带宽有严重的限制。该项目旨在开发自动化机器学习，以近真实的时间检测和分类感兴趣的目标，从而大大降低图像分析成本，并在自主遥感应用中开放使用这种系统。然而，这些在大量数据可用的情况下是无效的-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包含很少出现的相关物体。也就是说，虽然成像声纳镜头捕获和后期注释的成本很高，但对象（例如鱼，水母等）的外观通常与使用传统RGB相机获得的外观相似。因此，研究将集中在开发机器学习算法，通过让它们学习其他成像域（源域）（例如传统的RGB）来帮助处理声纳图像（目标域）。所开发的算法将属于“深度学习”算法家族，这是一种复杂的机器学习技术，最近已被证明可以在许多计算机视觉应用中提供阶跃变化。这将需要一个大型的带注释图像数据集进行训练，并需要Cefas中提供的图像外观方面的专家知识。随着研究的进展，学生还将为Cefas声纳的部署做出贡献。NEXUSS CDT提供最先进的，高度体验式的培训，用于环境科学的尖端智能和自主观测系统的应用和开发，以及全面的个人和专业发展。学生将有广泛的机会，通过与学术，研究和工业/政府/政策合作伙伴的广泛网络的互动，扩大他们的多学科视野。该学生将在东安格利亚大学注册，在图形，视觉和语音实验室的计算科学学院托管。学生将接受与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.