Beyond a shadow of a doubt: land surveying in the real world

毫无疑问：现实世界中的土地测量

• 批准号: 2882732
• 金额: --
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882732
• 关键词: Beyond; shadow; doubt; land; surveying

Scientific BackgroundIn our increasingly digitized world, computer vision technology plays a crucial role in a multitude of sectors, from agriculture to coastal monitoring. However, these systems often struggle in environments with variable lighting, particularly when dealing with shadows. This presents a significant challenge in real-world applications, leading to inaccuracies in environment classification and other essential tasks. Our project, in collaboration with the Centre for Environment, Fisheries and Aquaculture Science (CEFAS), seeks to tackle this problem head-on, aiming to develop advanced shadow-invariant processing algorithms that will greatly enhance computer vision capabilities. This timely research aims to bridge the gap between human visual competences and computer vision systems, a pioneering step towards increased accuracy and efficiency in sectors reliant on land and coastal surveying.Research MethodologyThe project will follow a comprehensive, hands-on research approach. The student involved will undergo training in measurement and calibration techniques, which will be applied to enhance vision systems used for remotely piloted aircraft. They will work closely with CEFAS to produce an annotated image set, identifying the same material both in and out of shadow regions. The primary focus will be to extend and adapt shadow invariant measurement techniques for the unique challenges posed by farm surveying and coastal monitoring. By incorporating Near Infrared (NIR) technology, the project will explore innovative ways to improve visual perception in shadowy environments. The research will take place both at the lab and in the field, offering a diverse and enriching experience.TrainingIn addition to gaining experience in cutting-edge computer vision research, the student will develop a robust set of skills in several key areas. They will receive training in the fundamentals of measurement and sensor calibration and get hands-on experience with field deployment of RPA (remotely piloted aircraft) systems. Through the development and implementation of algorithms, they will hone their programming and data analysis skills. They will also be exposed to interdisciplinary collaboration, working closely with professionals from agritech, geophysics, ecology, and computational science sectors. Their scientific research skills will be enhanced through the production of academic papers and contribution to public domain source code. This project offers a unique springboard for a future inter-disciplinary career in the AgriFoRwArdS area, providing a rich blend of theoretical knowledge and practical, industry-relevant experience.Join us as we redefine the frontiers of computer vision technology, paving the way for more accurate, efficient, and reliable environmental surveying solutions. Your participation could help shape the future of agricultural and coastal monitoring systems, making a lasting impact on these crucial sectors.

科学背景在我们日益数字化的世界中，计算机视觉技术在从农业到沿海监测的众多领域中发挥着至关重要的作用。然而，这些系统通常在光照变化的环境中表现不佳，尤其是在处理阴影时。这在现实世界的应用中提出了重大挑战，导致环境分类和其他基本任务不准确。我们的项目与环境、渔业和水产养殖科学中心（CEFAS）合作，寻求正面解决这个问题，旨在开发先进的阴影不变处理算法，以极大地增强计算机视觉能力。这项及时的研究旨在弥合人类视觉能力和计算机视觉系统之间的差距，这是提高依赖陆地和沿海测量的部门的准确性和效率的开创性一步。研究方法该项目将遵循全面的实践研究方法。参与的学生将接受测量和校准技术的培训，这些技术将用于增强遥控飞机的视觉系统。他们将与 CEFAS 密切合作，生成带注释的图像集，识别阴影区域内外的相同材料。主要重点是扩展和调整阴影不变测量技术，以应对农场测量和沿海监测带来的独特挑战。通过结合近红外（NIR）技术，该项目将探索创新方法来改善阴影环境中的视觉感知。研究将在实验室和现场进行，提供多样化和丰富的经验。培训除了获得尖端计算机视觉研究的经验外，学生还将在几个关键领域培养一套强大的技能。他们将接受测量和传感器校准基础知识的培训，并获得 RPA（遥控飞机）系统现场部署的实践经验。通过算法的开发和实现，他们将磨练编程和数据分析技能。他们还将参与跨学科合作，与农业技术、地球物理学、生态学和计算科学领域的专业人士密切合作。他们的科学研究技能将通过学术论文的发表和对公共领域源代码的贡献得到提高。该项目为 AgriFoRwArdS 领域的未来跨学科职业提供了一个独特的跳板，提供了丰富的理论知识与实践、行业相关经验的融合。加入我们，我们重新定义计算机视觉技术的前沿，为更多的领域铺平道路准确、高效、可靠的环境测量解决方案。您的参与可以帮助塑造农业和沿海监测系统的未来，对这些关键部门产生持久影响。