Vision AI does not only replicate human vision but can also go beyond that in offering highly accurate accounts of environmental features that are not readily visible to the human eye. However, while edge AI has been around for a while, enhancing edge capabilities with computer vision is still a novelty. Those who have ventured into improving production processes, safety, and quality with the help of vision AI, however, are already reaping the benefits.
When equipped with vision AI, industrial enterprises can take full control of their assets on the edge and build a truly collaborative foundation for a multitude of use cases. This will allow them to tackle the challenges of a dynamic setting that includes many unknowns.
What Vision AI Can Do for You
What makes vision AI so attractive for industrial deployments? It is not simply the high accuracy and adaptability of vision AI algorithms at the edge but also the sustainability of adopting computer vision applications. Below is a breakdown of what you can expect.
You Only Install the Hardware Once
Once you have installed and set up your smart camera system at the IoT edge and have connected to your platform, you are good to go. Taking it from there, you can use this solid foundation to build a variety of use cases on top of your existing solution and add or switch between different vision AI apps without disrupting ongoing operations.
No Need to Change Existing Assets
Using your existing legacy assets at the edge, you can use the platform to train, deploy, and improve on ML models without being dependent on specific types of hardware, IT specialists, or external providers. This means that your existing assets stay where they are — and you can use any hardware-agnostic platform to orchestrate assets within your device landscape.
No Need to Buy Additional Sensors for New Use Cases
Equally so, it is not necessary to buy new sensors or any new hardware if you want to build and test additional use cases. Once you have your hardware in place, you can easily adapt to changing demands by simply installing new apps (that is, machine learning models packaged as ready-to-use IoT applications), testing, debugging, and updating from within one platform with a full overview of what happens.
You Can Add Extra Apps Immediately or Incrementally
Adding, removing, and improving applications is equally seamless. You can add multiple apps at once to track various performance parameters and serve a variety of use cases. And you can transition between different levels of complexity gradually, adding and testing one app at a time.
Handle Complex Environments
Platform-enabled vision AI applications are at their best when they have to work in complex environments and capture a variety of issues that may evade human inspection. Using smart cameras installed at the edge, you can get vision AI apps to inspect barcodes, check for assembly or packaging errors, detect safety issues, count people on the shop floor, and perform inventory management all at the same time, with high levels of precision.
Allows for Seamless Scaling
By using just one platform and building apps, you create a solid foundation for your use cases, starting with data import to ML model creation and deployment back to the AI edge. Thanks to containerized applications, testing and deployments within production environments can take place without disruption, minimizing downtime.
The Case for Vision AI in Industrial Manufacturing
Just about every step of the manufacturing process can be enhanced and made more secure by implementing vision AI. Even more automation, real-time defect detection, and productivity tweaks are just a few of the possibilities. Below are some ideas of what you can achieve with computer vision.
Powered by vision AI, manufacturers can make sure processes are optimally orchestrated and executed. Waste reduction is at a minimum and the operating conditions on the shop floor are nearly ideal.
This makes for maximal visibility and less dependence on human expertise. Quality control is taken over by smart algorithms specifically trained to detect outliers, perform analyses, and send out alerts.
Root Cause Analysis
Computer vision at the IoT edge can help detect patterns that have eluded human observers. And it can implement complex action plans to tackle the ramifications of these discoveries. In unearthing the root cause for a specific phenomenon or behavior, manufacturers learn from errors quickly and avoid future mistakes.
When multiple errors occur, vision AI applications installed at the industrial edge can trace the chain of events and provide important insights to help remedy the situation. Inspection via computer vision is especially valuable when looking for certain manufacturing defects across the entire value chain. Here, as well, vision AI proves to be more accurate than the human eye.
Full Automation for Complex Visual Tasks
Because manufacturing processes are inherently complex and dynamic, achieving even modest levels of automation has been a challenge throughout. With the backing of a comprehensive IoT and AI platform, vision AI applications at the edge can respond to these dynamics. They continuously adapt to new variables.
New incoming IoT data can constantly deliver new insights and the ML models at the edge can be quickly adapted by rolling out instant OTA updates. This way, the automation cycle is continuously renewed without disruption.