Responsive and Robust Object Detection for Industrial Point Cloud Applications

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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.