Siemens Digital Industries Software’s SynthAI service is delivering the power of machine learning (ML) and artificial intelligence (AI) to solve the challenge of training machine vision systems.
“We were looking for a quick and easy solution that will enable us to detect wire terminals in a robotic electric cabinet assembly station. With SynthAI, our control engineers were able to achieve great results within just a few hours,” said Omer Einav, CEO of Siemens’ client, Polygon Technologies. “The tedious task of annotating a large set of training images to train the model was shortened significantly. The results show great promise for many additional use-cases we plan to handle with SynthAI.”
Machine learning is used for a variety of vision-based automation use-cases such as robotic bin picking, sorting, palletising, quality inspection and more. While usage of machine learning for vision-based automation is growing, many industries face challenges and struggle to implement it within their computer vision applications. This is due to 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, utilising synthetic data for vision use-cases requires expertise in synthetic image generation and can be complex, time consuming and expensive. This is where Siemens’ SynthAI changes the game.
Rather than waiting for pre-production 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 randomised 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 a 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 organisations prefer to handle training of their own systems, complete synthetic image datasets together with the annotations are also available.
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