{"id":5721,"date":"2022-01-11T16:42:41","date_gmt":"2022-01-11T21:42:41","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/?p=5721"},"modified":"2026-03-26T07:06:40","modified_gmt":"2026-03-26T11:06:40","slug":"ramping-up-machine-learning-and-vision-based-automation-with-synthetic-data","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/ramping-up-machine-learning-and-vision-based-automation-with-synthetic-data\/","title":{"rendered":"Ramping up machine learning and vision-based automation with synthetic data"},"content":{"rendered":"\n

Use synthetic data to accelerate machine learning quickly and easily for vision-based automation systems<\/strong><\/h3>\n\n\n
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\"SynthAI<\/figure><\/div>\n\n\n

Synthetic data is about to transform artificial intelligence. Today, machine learning is used for a variety of vision-based automation use cases like robotic bin picking, sorting, palletizing, quality inspection, and others. \u00a0While usage of machine learning for vision-based automation is growing, many industries face challenges and struggle to implement it within their computer vision applications. \u00a0This is due in large part to the need to collect many images and the challenges associated with accurately annotating the different products within those images.<\/p>\n\n\n\n

One of the latest trends in this domain is to utilize synthetic data<\/span><\/a> to speed up the data collection and training process. Synthetic data is typically regarded as any data that is generated by a computer simulation.<\/p>\n\n\n\n

However, utilizing synthetic data for vision use cases requires expertise in synthetic image generation and can be complex, time consuming, and expensive.  In addition, while some techniques and best practices for employing a machine learning model trained with synthetic data in real life already exist, these techniques are not yet commonly practiced.<\/p>\n\n\n\n

There needs to be an efficient method to bridge the skills traditionally required for a vision system such that it can be trained and deployed.  Such skills include data collection and annotation, machine learning model training and validation, and integration into the complete automation system.<\/p>\n\n\n\n

Providing an automated way to address the above tasks is key to scaling up the technology and making it accessible and cost-effective.  The good news is that there is a way to do it<\/span><\/a>!  Read on to learn how.<\/p>\n\n\n\n

By 2024, 60% of the data used for the de\u00advel\u00adop\u00adment of AI and an\u00ada\u00adlyt\u00adics projects will be syn\u00adthet\u00adi\u00adcally generated<\/em>.<\/strong><\/p>WSJ<\/span><\/a>, quoting Gartner Inc.<\/cite><\/blockquote>\n\n\n\n

The potential of synthetic data for machine learning-based vision systems<\/strong><\/h3>\n\n\n\n

The AI for machine vision market is expected to reach $25B by 2023<\/a><\/span> with a CAGR of 26.3% (source MarketsandMarkets<\/span><\/a>). \u00a0This market consists of industry use cases such as kitting, sorting, picking, shopfloor safety, throughput analysis, quality inspection, and many more. \u00a0For instance, vision systems utilize objection detection algorithms to automatically recognize the position of objects and guide a robot to pick them up. <\/p>\n\n\n

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\"Robot
A robot picking up metal parts from a bin based on camera input.<\/figcaption><\/figure><\/div>\n\n\n

What are the steps for object detection?<\/strong><\/h3>\n\n\n\n

To understand the potential of synthetic data, let’s review the workflow of deploying a typical object detection vision system:<\/p>\n\n\n

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\"Computer
Typical workflow for the deployment of vision-based object detection systems.<\/figcaption><\/figure><\/div>\n\n\n

Synthetic data can help with shortening this workflow and making it more robust by addressing some of the pain points in the data collection and annotation stages:<\/p>\n\n\n\n