Siemens Digital Industries Software’s SynthAI service is delivering the power of machine learning and artificial intelligence. Accordingly, the objective is to solve the challenge of training machine vision systems.
Omer Einav, Chief Executive Officer at Polygon Technologies, noted companies like them tend to look for easy solution to allow them detect wire terminals in a robotic electric cabinet assembly station. For that reason, their control engineers utilize Siemens’ SynthAI to achieve great results within just a few hours.
“The tedious task of annotating a large set of training images to train the model shortened significantly. The results show great promise for many additional use cases we plan to handle with SynthAI,” said Einav.
A variety of vision-based automation use cases, such as robotic bin picking, sorting, palletizing, quality inspection, among others employ machine learning. While usage of machine learning for vision-based automation is growing, many industries face challenges to implement it within their computer vision applications.
This is because of the need to collect many images of the parts in question and the challenges associated with accurately annotating the different products within those images – particularly before production or manufacturing begins.
To solve this challenge, synthetic data is used to speed up the data collection and training process. However, utilizing synthetic data for vision use cases requires expertise in synthetic image generation and can be complex, time consuming, and expensive. This where Siemens’ SynthAI changes the game.
Rather than waiting for preproduction parts to be ready or using complex processes to generate synthetic data, machine vision specialists only need to provide 3D CAD data of the parts. SynthAI will then automatically generate thousands of randomized annotated synthetic images within minutes without the specialist knowledge typically required.
SynthAI will also automatically train a machine learning model that could be used to detect your product in real life. Once the training is done, the trained model can be downloaded, tested, and deployed offline – using no more than a little Python coding. If organizations prefer to handle training of their own systems, complete synthetic image datasets together with the annotations are also available.
“The market for Artificial Intelligence for Machine Vision is expected to reach US$25B by 2023, but there are many challenges facing those looking to take advantage of its benefits,” said Zvi Feuer, Senior Vice President and General Manager Digital Manufacturing at Siemens Digital Industries Software. “SynthAI demonstrates how Siemens is taking its depth of knowledge in both product engineering systems as well as production preparation and planning and finding room for innovations that allow our customers to take advantage of tomorrow’s technology, today.”