Computer Vision Is Emerging As The Next Frontier For Quality Control
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Computer Vision Is Emerging As The Next Frontier For Quality Control

Applications based on computer vision (CV) are making their way in a lot of industries today and minimizing human intervention. They are optimizing operational efficiency and reducing unnecessary labor costs. By feeding an AI system with thousands of images & training videos, it is possible to make the AI system differentiate between what is acceptable and what is not. With deep learning, computer vision can witness compounded levels of progression over time.

Quality Matters!

Locating quality deficiencies is a constant process and is the pillar of preventing customer dissatisfaction. It can also avert damage to the production lines. To maintain the highest quality standard, there must be an eagle-eyed and constant inspection in place, which has traditionally been manual in several industries.

Now, enter the age of computer vision! A mix of image processing and advanced machine learning approaches allows for distinguishing specific/known objects from foreign objects.

How Does Computer Vision Work?

Machine vision comprises digital cameras, lighting, and optics, which are connected with software that processes the captured images. The system’s “brain”, the AI/ML powering it, evaluates the images and then initiates an action after performing data analysis. 

When it comes to the hardware, cameras, lighting systems, etc, have progressed a lot over the years. It’s not a difficult task to capture detailed image information, even under challenging circumstances nowadays. 

For instance, if an image fails to have adequate contrast under white light, banks of LEDs can be deployed to strobe through. A satisfactory and workable wavelength can be found in milliseconds and intensity can be adjusted.

When a workable image has been captured, the pixels are allocated specific numerical values. After assigning numerical values to each pixel, the “brain” probes for patterns & edges, combining the edge information to illustrate forms. 

For example, consider identifying bolt holes and precisely placing parts of machinery. It is important to inspect for the correct sizes in this scenario and an automated vision system aptly does it.

Computer Vision Implementation: Examples

With a CV-powered system, manufacturing enterprises do not require active personnel to review manufactured products. Furthermore, such a system rarely (almost never) makes a mistake, which is otherwise common in the manual inspection. 

1. Surface Imperfection Detection

Shortcomings in a material’s surface are considered an aesthetic issue. However, customers are increasingly associating the first-look or the appearance of any product with quality. This is making more and more manufacturers concerned with the look of a product. 

Automated visuals, run by AI, can contribute massively in identifying defects, inspection, understanding functional flaws, packaging of a product, and much more with ZERO human involvement. 

Proper identification of minute surface deformation can prohibit its usage in several scenarios at an earlier stage. For example, scratches, loss of surface uniformity, etc can be indicators of a poor product.

Computer vision can guarantee the quality of any manufactured item if it knows how to detect the smallest of irregularities. In addition to that, advanced computer vision tech can not only compute metrics such as the shape, type, or the proportions of a defect but also classify them under categories. 

While some forms of defects are tolerable, products with serious defects can be red-flagged for the omission. The data (for defective items) collected by a computer vision system, when analyzed, can also aid in identifying the reason behind the defect.

2. Foreign Object Detection

In any production/packaging line, specific assumptions are made for the incoming materials or items. The manufacturing machines are fine-tuned to efficiently carry out tasks based on those assumptions.

If a foreign object enters the machine, it can potentially damage the internal parts of the machinery or, at the very least, upset the consumers at a later stage.

As a result, every foreign object must be detected in order to initiate preventative actions. This is where computer vision comes in again! 

For example, let’s imagine a packaging line for apples. If a stone somehow manages to get along with the harvested apples, it must be separated prior to any further treatment. 

Unlocking The Potential Of Smarter Machines With CV

Artificial intelligence (AI) has grown leaps and bounds over the years. Today, a programmer can decide the features that should be a part of the fundamental inspection characteristics. 

For traditional vision software, it is quite easy to identify a sprocket, thanks to its well-defined geometry with little or no fluctuation. But, what about items having a broad range of appearances? AI systems can apprehend disparities even without being explicitly programmed if they are fed with a substantial dataset.

CV systems, when combined with deep learning, form a potent solution. Deep learning utilizes neural networks, having thousands of layers that are great at mimicking human-level intelligence. 

They can distinguish between anomalies, parts, etc and tolerate variations in complicated patterns (a key advancement). Thus, deep learning inches a CV-system closer to human adaptability while conducting a visual inspection, albeit, with greater speed and robustness.

Since CV systems are mostly associated with manufacturing, let’s take an agricultural application into consideration. Weed control is necessary for agriculture and an ML-powered CV system can be deployed to identify and spray weed-controlling herbicides.

An integrated system of cameras can check each plant as tractors pull herbicide sprayers over the fields. After determining whether it’s a valuable plant or a troublesome weed, the system can automatically apply herbicide to the latter.

The CV-based spraying technique can reduce herbicide expenditures by up to 90% in comparison to the shotgun technique of crop dusting. By administering micro-doses of herbicide only to the weeds, leaching of toxic chemicals(herbicide) can be averted as well.

Closing Note

Artificial intelligence (AI) is now enabling computer vision (CV) systems to be applied in unique ways. It is increasingly being used in a variety of areas to improve quality control processes by automating them and making the entire process more efficient and cost-effective.

Even if you fail to afford full robotic automation, a CV-based system can still help with quality control by relentlessly identifying issues. From freeing up manufacturing inspectors to resource optimization, the benefits are manifold. To learn more about computer vision and how it can transform your business efficiency, connect with our experts today!

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