Computer Vision and Picture Perfect Quality Control in the Era of Industry 4.0

August 10, 2022


Explore how Industry 4.0 leverages Computer Vision for enhanced Quality Control. Dive into its impact on manufacturing efficiency and how Wallaroo aids in effortlessly deploying complex models.

Digitalization has come a long way with the turn of the 4th Industrial Revolution, also known as “Industry 4.0”. The recent developments in Computer Vision and its applications within quality control show how this Smart Industry continues to leverage machine learning to further automate and digitalize as many industries as possible. 

Using Computer Vision in QC can increase yield at each step of the manufacturing process, leading to compounding gains with even just a few basis points improvement per step. With computer vision, you can look at everything coming off the line and detect things the human eye cannot. Not only this but you can enable non-stop quality checks and monitoring at a cheaper cost compared to hiring dedicated resources for the task. The algorithms used can perform multiple inspections at high speed and can alert quality control to take action when irregularities are detected. 

Can Computer Vision replace Human Vision for Quality Control?

Compared to human vision, computer vision has many more advantages than the human eye and brain are capable of when it comes to quality control. The image analysis algorithms it uses can identify irregularities and detect issues that a human simply wouldn’t be able to by using tools such as infrared. In fact, AI-based solutions can increase defect detection by 90%, compared to human inspection. Computer vision based solutions can also help avoid future defects aiding in root cause analysis to reveal what parts of the value chain need adjustments. 

The Di-Vision holding us back from widespread usage

Despite the long list of applicable uses and potential returns many have yet to integrate this new technology due to some of its challenges when placed in production. Failure to properly configure the hardware system (typically cameras) and software algorithms can leave a significant blind spot. While computer vision models are getting better at quality tasks they are also getting bigger. The bigger the model, the more parameters it has, and therefore the higher the number of computations it needs for inference. This can be computationally demanding and require optimized memory architecture for faster access. For example, a semi-supervised learning approach Noisy Student (by Google) relies on over 480M parameters to process images and requires immense computing power. 

Machine learning models, in this case, computer vision, are constantly improving and finding they can outperform humans in certain tasks, but as these models’ capabilities increase, so does their inference time. Those with access to GPU-powered devices may be able to easily tackle these challenges, but for most, they only realize during the later development stages that the model demands too much computing power. It is great if we just want to keep improving these models for research, but we want to use it for real-world applications to solve real-world problems. One of the major challenges now is to meet this real-time demand on the computationally limited platforms available.

How can Wallaroo help?

Instead of reducing the quality of your original model to fit your current architecture, you can make the most of your model by running on a platform like Wallaroo with a high-performance compute engine purpose-built for machine learning. Wallaroo was designed with the last mile of the ML process in mind so businesses can easily deploy complex models without accepting mediocre performance. The platform is built around a scalable Rust engine that specializes in high-volume computations. You can deploy models in seconds without interruptions or time-consuming model optimization steps, as well as integrate complex models using your endorsed reporting and storage tools. 

It’s also designed to integrate smoothly into your data ecosystem and can be run on-prem, in the cloud, or at the edge (see here for a more in-depth discussion of what it takes to deploy and manage ML  models at the edge). With Wallaroo scalability is not an issue, the platform can help you handle large volumes of data without an equally large cost to your current infrastructure, making it possible to scale operations with ease. Some customers were able to double inference throughput and cut latency almost in half, while others have seen up to 80% reduction in the cost of computational resources. Allow Wallaroo to help your business effectively deploy, monitor, and optimize your most complex computer vision models with ease.

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