Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

How Visual Object Detection Can Transform Manufacturing Industries

DZone's Guide to

How Visual Object Detection Can Transform Manufacturing Industries

Object Detection is a basic visual perception task and one of the key areas of applications of Computer Vision. We believe that recent advances in AI will help accelerate this trend towards manufacturing.

· AI Zone ·
Free Resource

EdgeVerve’s Business Applications built on AI platform Infosys Nia™ enables your enterprise to manage specific business areas and make the move from a deterministic to cognitive approach.

Since the industrial revolution, humanity has made tremendous progress in manufacturing. With time, we have seen more and more of mundane manual work being replaced by automation through advanced engineering, computers, robotics, and now IoT. We believe that recent advances in AI (or Deep Learning to be more precise) will help accelerate this trend towards automation in a fascinating way. This is because AI adds one very critical component that the factories have been missing until today — "The ability of machines to see". With computer vision-enabled robots, a lot of new and unexplored territories of automation can now be explored. "Object Detection" is a branch of Computer Vision that deals with finding specific objects (like humans, RedBull Cans, cartons of RedBull Cans, etc.) from an image. With this article, we will make a case about why Object Detection is a key building block for manufacturing automation and how you should think about it.

What Really is Object Detection?

Computer Vision is the field that deals with empowering the computer's ability to "see" things like humans. Object Detection is a basic visual perception task and one of the key areas of applications of Computer Vision. It essentially deals with finding and locating specific objects within an image.

For detecting generic objects (like car, person, table, tree) there are open-source and pre-trained models like Yolo available. However, if you want an algorithm to detect very specific objects (like a "small raw tomato" or a "large ripe tomato"), you will need to train an object detection algorithm of your own.

Use Cases of Object Detection in Manufacturing

Finding a specific object through visual inspection is a basic task that is involved in multiple industrial processes like sorting, inventory management, machining, quality management, packaging, etc. In this blog, we discuss few such use cases to help the reader build an intuitive understanding of how this technology can be applied in any new manufacturing environment.

Quality Management

Until recently, the quality control part of the manufacturing cycle continues to be a difficult task due to its reliance on human-level visual understanding and adaptation to constantly changing conditions and products. With AI, most of these complications can be handled. AI can automatically distinguish good parts from faulty parts on an assembly line with incredible speed, allowing you enough time to take corrective action. This is a very useful solution for dynamic environments where product environments are constantly changing and time is valuable to the business.

Inventory Management

Inventory management can be very tricky, as items are hard to track in real-time because something is always added, removed, and moved every day. Poor Inventory management can hurt the company both in terms of capital and time. AI systems can perform automatic object counting and localization that will allow you to improve inventory accuracy. AI automation removes human error from the equation by accurately counting your holding and outgoing inventory. When automated, businesses will order the right quantity of products at the best possible price, ensuring that no money is wasted on inaccurate or extraneous orders.

Sorting

Manual sorting involves high cost of labor and accompanying human errors. Even with robots, the process is not accurate enough and is still prone to a discrepancy. With AI-powered Object Tracking, the objects are classified as per the parameter selected by the manufacturer and statistics of the number of objects is displayed. It significantly reduces the abnormalities in categorization and makes the assembly line more flexible. For example, in Agriculture industries, Sorting plays a critical role in the assembly line. It is imperative for the company to identify and discard damaged fruits/vegetables, which can affect the finished product. AI-powered Object Detection can help transform this tedious and manual process into an efficient and automated process while maintaining the same if not better level of accuracy.

Assembly Line

Today, we have fully automated assembly lines even for complex products like cars. However, each movement of robotic arms and raw materials/components are defined and played as per a script. To give the modern automatic assembly line more flexibility, it is important to teach machines to locate and move different products/components accurately. AI-powered object detection opens the doors towards this possibility.

How Does Custom Object Detection Work?

There are several challenges that need to be taken into account when performing customized object detection for a niche use case in a manufacturing set-up. Objects come in different shapes, sizes, orientation, colors, and a real-world factory environment has additional noise coming from variation in viewpoint, illumination, occlusions, and shadows. On the algorithm side, you need to ensure that the desired accuracy is achieved without the need for arranging too many (in order of thousands) of training examples.

Adopting a digital strategy is just the beginning. For enterprise-wide digital transformation to truly take effect, you need an infrastructure that’s #BuiltOnAI. Click here to learn more.

Topics:
ai and machine learning ,deep learning ,object detection

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}