Production line automation is one of the most important steps in optimizing production processes. Advances in image processing technology create new opportunities to enhance machine vision to increase the quality and efficiency of production lines across a variety of industries. A typical machine vision-based image processing system consists of cameras, lenses, lights and cables, as well as processing units and control electronics that synchronize and control the production line. Image sensors are the core device in cameras, functioning as the eyes for inspection and monitoring of the production line. As such, the performance and functionality of image sensors greatly influence the overall performance of the system.

1. Resolution
2. Sensitivity and SNR
3. Dynamic range
4. Number of frames per second (frame rate)
5. Shutter type
6. Colorful or monochrome

To improve the quality and efficiency of production systems, the most common machine vision applications that use image processing include inspection, control, and identification.

Both companies and consumers expect impeccable quality in all types of products across different industries. Therefore, all products must be inspected during the production process. In addition, manufacturers need to accurately analyze the number and causes of rejected products to improve quality. These requirements can be met with the use of image processing technology.

How should products be inspected?

The first approach is to measure the object optically, capturing its image with machine vision and analyzing it with a dedicated algorithm. Higher camera resolutions and reduced optical distortion from lenses produce more accurate results. However, the laws of optical physics limit the theoretical accuracy of optical measurements compared to physical measurements.

On the other hand, a standard industrial camera system can scan individual objects at a speed of tens to hundreds of frames per second, which makes optical measurement technology excellent in terms of speed. Machine vision is also the only reliable method for inspecting printed materials such as labels and barcodes.

The second approach is the inspection of natural and machined surfaces, checking for scratches, processing flaws and contamination. The third is the inspection of material color and texture to ensure uniform quality and appearance in packaging and materials.

The second major application is production line control. The image analysis results of the machine vision system are used to adjust the control parameters of production equipment. For example, in automobile production, the camera checks the amount and location of the adhesive applied by robots. If there is a deviation from the target value, the robot adjusts the application according to the instructions of the image processing program.

This principle also applies to bottling (filling) plants, where the camera monitors the fill level of the bottle and adjusts the quantity when there is a difference between the desired and actual value. In the sorting and recycling of glass and other waste, the image processing system controls the air output so that glass pieces of different colors are directed to separate sorting lines. Assembly and logistics robots use industrial cameras and control systems with high processing power and reliable algorithms to adjust their orientation, enabling precise picking and positioning of objects.

Machine vision enables many of these applications while reducing costs and improving quality. It also helps to increase safety in the production environment, an increasingly important initiative. Image processing detects the presence and exact location of people, alerts to hazards and automatically shuts down the production line when necessary.

Logistics and production of complex products require the processing of hundreds, and sometimes even hundreds of thousands, of different objects. Each item needs to be identifiable to enable advanced automation. One-dimensional and two-dimensional barcodes (QR codes and data matrix codes) are used for identification. More information can be encoded in a reduced space with two-dimensional codes. Laser scanners equipped with linear image sensors are often used to read one-dimensional barcodes.

In addition to reading 2D codes, camera-based systems can collect additional information, such as the size and position of objects. Electronic components often do not have enough space to hold printed 1D or 2D codes. Therefore, optical character recognition (OCR) is used to identify alphanumeric codes.

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