Democratization of Computer Vision: Revolutionizing Manufacturing Applications

Innovations in automation have been significantly propelled by the progress in computer vision technology. This technology has found its applications in diverse areas, from security and crowd control to autonomous driving. Specifically, in the manufacturing sector, the democratization of computer vision, fueled by the progression in deep learning techniques, has been a game-changer. This piece aims to shed light on the influence of computer vision in manufacturing applications, without delving into the mathematical or algorithmic complexity that underlies computer vision.

Historically, computer vision was not readily available for everyday applications due to the high computational cost and rudimentary nature of early neural network algorithms. The infancy of computer vision was marked by basic systems, such as low-resolution video inputs, that were both limited in their capabilities and often too expensive. These systems heavily relied on handcrafted features and shallow learning models, necessitating substantial expertise and computational resources for implementation and operation. The outcomes of these systems worked well in controlled settings, making them more academically oriented rather than practical for factory use.

However, the introduction of deep learning and the substantial increase in computational power over the past decade have triggered a significant shift. Deep learning techniques have shown great efficacy in computer vision tasks due to their ability to learn intricate patterns from extensive data. Coupled with the reducing cost of computation, computer vision has become more accessible and applicable across various industries. The growth of Open-source Computer Vision projects, which often match or even surpass commercial tools, has also been a substantial contributing factor.

Case Examples:

We have taken part in numerous initiatives that leverage computer vision to augment manufacturing processes. For example, we have employed computer vision to assess the results of Label Rub tests, providing a more dependable and consistent alternative to human examination. This has greatly enhanced the evaluation process, ensuring that only the top-quality products reach our customers.

Another application of computer vision we have explored involves identifying surface defects on reusable bottles. Traditionally, this process would necessitate thorough manual inspection, which is both time-consuming and susceptible to human error. With computer vision, we have been able to automate this process, enhancing efficiency and precision.

We have also implemented computer vision to maintain snack filling uniformity. In the food manufacturing industry, consistency is critical for both quality control and regulatory standards. Computer vision has given us a reliable way to monitor and maintain filling levels consistency, leading to better product quality and customer satisfaction.

These are a few instances of how the widespread adoption of computer vision is transforming manufacturing applications. As we continue to make progress in this field, it is essential to ensure that these technologies are used responsibly and ethically. The potential of computer vision in manufacturing is tremendous, and the future is definitely very bright

For Further Reading

For Python developers who are interested in delving deeper (I'm sure many of you have already done so), some compelling open-source projects include OpenCV, Pytorch, and TensorFlow. These libraries are also available in Julia and are known for their speed and power. We are still exploring more in Julia and have not yet worked on any commercial projects. For prototyping, we have found Matlab to be extremely useful. Although it's not free, it provides great value for money, especially for prototyping complex applications.

Here are some useful links:

OpenSource Computer Vision: https://opencv.org/

PyTorch, a machine learning framework used for applications like computer vision and natural language processing, initially developed by Meta AI: https://pytorch.org/

TensorFlow, an end-to-end open source platform for machine learning: https://www.tensorflow.org/

Computer Vision Toolbox, providing algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems: https://in.mathworks.com/products/computer-vision.html

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