May 10th, 2023 | Published in Research
AI-Platform for automated training of object detection models based on cad data
The manual assembly of devices consisting of many individual parts is a time-consuming, tedious and error-prone industrial process, which could in principle be supported by automated recognition technologies. As a typical example, Gabler is a manufacturer of production machines for packaging goods, each consisting of thousands of individual parts, either retrieved from an internal warehouse or obtained from external suppliers.
The part identification process could be assisted through automated object detection within an Augmented Reality (AR) application which outputs a list of potential candidates that limits the number of objects to check: The identification process becomes faster and easier, which saves the employee several minutes per identification task and the company multiple productive hours per day, in addition to reducing frustration within the workforce and avoiding errors in production.
However, while vision-based part recognition based on Deep Learning provides an easy and fast solution to the industrial challenge, the traditional way of building such tools is not cost-effective: huge amounts of images with corresponding labels are needed to train object recognition models. This is time-consuming and costly, thus severely limiting many industrial applications in terms of the number of different objects: Currently, only a few specific industrial applications such as autonomous robots or mass production quality control can benefit. Applications such as warehouse part detection, where one is dealing with thousands of individual parts, are still not economically feasible. Kimoknow seeks to speed up and simplify the generation of these AR applications in order to offer them to a much broader market, including companies like Gabler.
Taking a broader perspective, better human-machine co-working will be one of the most important changes that can facilitate the 4th industrial revolution. Vision-based assistant systems are an important component of that. AI-based computer vision has progressed considerably, but the efficient and cost-effective generation of machine learning datasets remains an open problem for these industrial use cases.
The infrastructure built that was developed together with TECO during the experiment provides Kimoknow with a business model that is highly scalable and can be backed by a cost-effective and energy-efficient HPC backend for the batched creation of fine-tuned object detection models in reduced time.