Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit

应用新颖的机器学习技术和高速 3D 视觉算法进行水果实时检测

• 批准号: 1962928
• 金额: --
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1962928
• 关键词: Application; novel; machine; learning; techniques

The student will use and develop advanced machine learning algorithms to measure, identify and detect fruit in real time and in 3D. The algorithms will be trainable (so that a range of fruit types can be identified) and provide a world x,y,z co-ordinate of the fruit. Kusmann, Duckett, Cielniak and Pearson (2017, J. Field Robotics in press) have developed similar systems for broccoli. This earlier work showed that 3D cameras could be deployed in field environments however the algorithms were highly complex with relatively slow processing speed. The new challenge for this PhD project will be to minimise processing requirements to identify fruit whilst maximising processing speed and recognition fidelity. This project will initially focus on strawberry and may include apple.

学生将使用和开发先进的机器学习算法来真实的实时和3D测量、识别和检测水果。这些算法将是可训练的（以便可以识别一系列水果类型），并提供水果的世界x，y，z坐标。Kusmann，Duckett，Cielniak和Pearson（2017，J. Field Robotics出版）已经为西兰花开发了类似的系统。这项早期的工作表明，3D相机可以部署在现场环境中，但算法非常复杂，处理速度相对较慢。这个博士项目的新挑战将是最大限度地减少识别水果的处理要求，同时最大限度地提高处理速度和识别保真度。该项目最初将侧重于草莓，可能包括苹果。