Responsive and Robust Object Detection for Industrial Point Cloud Applications

适用于工业点云应用的响应灵敏、鲁棒的物体检测

• 批准号: 567583-2021
• 负责人: Najjaran, Homayoun
• 金额: $ 1.46万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722623
• 关键词: Responsive; Robust; Object; Detection; Industrial

Our research objective is to create practical three dimensional or shape-based object detection methods that can support high precision industrial applications such as metrology and visual quality inspection. Automated object detection has been a long-standing topic in both academic research and industrial applications. Although end-to-end machine learning algorithms can adaptively be used to detect the objects in 3D point clouds, it requires a large amount of training dataset to perform well. Also, due to algorithmic complexity, it can be too slow for real-time applications. At the same time, classic statistical computer vision methods are proved to be less computationally expensive and have satisfying performance even with a small amount of dataset. However, these methods are not responsive and adaptive in most cases. To achieve the required accuracies and computation performance, it is required that a combination of both the state-of-the-art machine learning methods and the classical statistical methods with their respective advantages are incorporated into a set of software solutions that can be optimally deal with different application scenarios. In this hybrid solution, the problem with the mentioned shortcomings of both methods can be solved. Instead of attempting to maximize detection capability, we aim at finding a balance among algorithmic complexity, robustness and accuracy by combining the aforementioned methods into a practical industrial solution to help higher-level point cloud exploration and decision making. The deployable software tools at the end of this project will impact the Canadian manufacturing sector and beyond, including practical uses in factories, warehouses, and surveillance systems across Canada. Canadian industry will also benefit from the robust object recognition technology developed for the system, which can be readily applied to the growing market of robotic and autonomous vehicle industry. Training of highly qualified personnel for such industries is another important outcome of this research.

我们的研究目标是创建实用的三维或基于形状的对象检测方法，以支持高精度工业应用，例如计量和视觉质量检查。在学术研究和工业应用中，自动化对象检测一直是一个长期的话题。尽管可以自适应地使用端到端的机器学习算法来检测3D点云中的对象，但它需要大量的培训数据集才能表现良好。同样，由于算法复杂性，对于实时应用程序，它可能太慢了。同时，经典的统计计算机视觉方法被证明在计算上的昂贵不足，即使使用少量数据集也具有令人满意的性能。但是，在大多数情况下，这些方法对响应性和适应性不足。为了达到所需的精确性和计算性能，要求将最先进的机器学习方法和经典统计方法及其各自优势的组合纳入一组软件解决方案中，这些软件解决方案可以最佳地处理不同的应用程序方案。在这种混合解决方案中，可以解决两种方法缺点的问题。我们旨在通过将上述方法组合到实用的工业解决方案中，以帮助高级点云探索和决策来实现算法复杂性，鲁棒性和准确性之间的平衡，而是试图最大程度地提高检测能力。该项目末尾的可部署软件工具将影响加拿大制造业及其他地区，包括在加拿大的工厂，仓库和监视系统中的实际用途。加拿大工业还将受益于该系统开发的强大对象识别技术，该技术可以很容易地应用于机器人和自动驾驶汽车行业的不断增长市场。对此类行业的高素质人员培训是这项研究的另一个重要结果。