When image processing makes sense

Image processing (or "machine vision") is a technology that enables machines to interpret and understand the visual world. It is often used in automation to enable machines to perform tasks that require visual perception and analysis.

Machine vision is often used in applications where something arrives at a station in a disorganized/imprecise manner: the robot needs to figure out where the material is.

However, it is important to note that vision is not always the best choice for automation. In some cases, there may be more effective or less expensive alternatives that can achieve the same result.

This article looks at the uses of machine vision in automation and discusses the importance of considering all available options and understanding the benefits and limitations of machine vision solutions.

What is machine vision?

So what exactly is machine vision? Machine vision is a technology that enables machines to interpret and understand the visual world. It is used to enable machines to perform tasks that require visual perception and analysis, such as inspecting objects, searching for individual objects in a disorganized container, reading labels or controlling robots.

A typical image processing system consists of a camera and software. The camera captures images or videos of the environment, which are then processed by the software to extract relevant information. The software analyzes the images or videos using algorithms and techniques such as pattern recognition and machine learning to understand what it sees. The results of this analysis are then used to perform a specific task or make a decision.

In addition to a camera and software, a machine vision system can also include other hardware components such as lighting, lenses and image sensors. These components work together to capture high-quality images or videos that can be accurately analyzed by the software.

Overall, machine vision is a powerful tool that enables machines to perceive and understand their environment so that they can perform tasks that would be difficult or impossible for humans.

What are common use cases for machine vision?

Machine vision is often used in production environments to improve efficiency, quality and safety. Some common use cases are:

1. Quality control: machine vision can be used to inspect products for defects to ensure they meet certain quality standards. This can help reduce waste and improve customer satisfaction by identifying and removing defective products before they reach the market.
2. Process optimization: Machine vision can be used to monitor and analyze production processes to identify bottlenecks, optimize workflows and reduce errors.
3. Material handling: Machine vision can be used to control robots and other automated systems in tasks such as sorting, packing and transporting materials.
4. Product identification: Machine vision can be used to identify and track products as they move through the production process, enabling real-time tracking and traceability.
5. Safety: Machine vision can be used to monitor production environments for hazards and ensure that safety protocols are followed. It can also be used for tasks that are too dangerous for humans to perform.

Overall, machine vision is a valuable tool for improving efficiency, quality and safety in production environments. It has the potential to revolutionize many different industries and sectors by enabling machines to perform tasks that would be difficult or impossible for humans.

What are common problems with machine vision?

There are a number of challenges that machine vision systems can face in a production environment, including

1. Changing lighting conditions: Machine vision systems rely on consistent lighting conditions to accurately interpret images. Changes in lighting conditions can cause problems with image quality and accuracy, which can lead to incorrect results or system failure.
2. Dust and dirt: Dust and dirt can build up on cameras and other image processing components and cause problems with image quality and accuracy. This can be particularly challenging in production environments where there is a lot of dust and dirt present
3. Difficult to detect workpieces: Some workpieces can be difficult to detect due to their size, shape, color or material. This can make it difficult for vision systems to accurately interpret images and perform tasks.
4. Transparency: Transparent materials can be difficult to recognize with machine vision because they allow light to pass through and may not produce a clear image.
5. Shadows: Shadows can lead to confusing or misleading visual information, making it difficult for machine vision systems to accurately interpret images.

To overcome these challenges, machine vision systems often use specialized lighting and optics, image processing algorithms and machine learning techniques to improve image quality and accuracy. In some cases, it may also be necessary to change the production environment or use additional sensors to improve the performance of the machine vision system.

Should I try to automate without machine vision?

Machine vision is a powerful and versatile tool whose capabilities have exploded in recent years. It is used in a wide range of applications and industries, from manufacturing and packaging to AMR control and even medical diagnostics.

However, it should be noted that machine vision systems can be sensitive and, if used incorrectly, are a much greater source of error. These systems rely on high quality images and accurate interpretation of those images to perform their tasks. Any problems with image quality or accuracy can lead to incorrect results or system failure.

In addition, vision systems can be complex and require specialized hardware and software, which can lead to higher costs and maintenance requirements. As mentioned earlier, they can also be sensitive to changes in the environment, such as lighting or dust, which can affect their performance.

It is therefore important to carefully consider whether vision is really necessary for a particular project or task. In some cases, it may be possible to automate a process using other technologies or methods that are simpler and more cost-effective. Before implementing a vision system, it is important to thoroughly review all available options and select the one that best meets the needs of the project.

Conclusion

In summary, machine vision is a powerful technology that has the potential to revolutionize many different industries and sectors. It enables machines to interpret and understand the visual world, allowing them to perform tasks that previously only humans could do. However, it is important to carefully consider whether machine vision is really necessary for a particular project or task.

But if machine vision is necessary, it is often the only option: you can either automate with machine vision or not automate at all. In these cases, it's important to expect the kind of advice you can expect from the machine vision experts at Unchained Robotics.