Looking at the rapidly evolving industrial automation sector, system integrators never stop improving the performance and reliability of machine vision systems. One of the most interesting developments in recent years has been the use of synthetic data. Synthetic data, if one is unfamiliar, pertains to artificially created data that resembles real-world conditions. Applied to vision applications, synthetic data brings with it a raft of benefits that can radically improve the outcome of projects. Here's why system integrators should consider seriously including synthetic data in their vision applications.
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One of the most time-consuming aspects of developing any machine vision system is gathering and annotating large data sets of real-world images. Whereas this could take weeks or months—influenced by the complexity of the environment and how many scenarios must be captured—a synthetic dataset can be generated very fast. At the forefront of these advanced techniques lie 3D rendering and domain randomization, which can go to the extent of including a wide range of conditions and variations within a synthetic dataset. This is significantly going to reduce the development cycle of system integrators, and with it, vision systems could be rolled out much faster.
Industrial environments in the real world are variable. Lighting, occlusions of objects, and changes in the appearance of the object can all affect the performance of a machine vision system. Synthetic data lets integrators generate datasets that cover a comprehensive sweep of these variables. Training models on very different, representative data makes the resulting vision systems more robust and better able to cope with the unpredictability of real-world conditions.
This is not only time-consuming but also very expensive. Setup, image capturing, and manual labelling could amount to tens of thousands quickly. Synthetic data provides an inexpensive alternative to these processes. Since synthetic images are algorithmically generated, the marginal cost of creating additional data points is close to zero. This means that integrators can produce large volumes of high-quality data without incurring the significant expenses associated with traditional data collection methods.
Very often, it is difficult, dangerous, or simply impossible to capture data in the real world. For example, training a vision system to recognize rare failures in the manufacturing process might mean waiting for sufficient numbers of faulty products to emerge from production lines, which may take months. Synthetic data allows integrators to easily go beyond this kind of limitation by generating realistic representations of such events. This brings down the dependency on scarce real-world data and provides a surety that models are trained on all the scenarios necessary, including those hard to replicate in the real world.
Ultimately, the goal of any systems integrator is to deliver solutions that produce a strong ROI for clients. By using synthetic data in vision applications, integrators can realize faster development times, increased robustness of the system, and reduced costs—all contributing to a better return on investment. Synthetic data may also mitigate such risks as delays or cost overruns during a project in order to deliver additional value to the client.
Synthetic data offers a headstrong tool that will enable system integrators to surmount the most thorny challenges standing in the way of implementing vision applications much better. By fast-tracking development, better model performance, and reducing costs, synthetic data will support integrators in delivering more effective and reliable solutions for clients. Those embracing synthetic data will be at the top of their game as the field of industrial automation continues to evolve.
