How to Compare Vision-Based Detection Systems for Food Processing

Figure 1: Objects on
a busy background
(left) and on a plain
background (right).

When it comes to detecting contaminants, cameras will likely miss items such as clear plastics or any objects similar in color to the product. Line speed and lighting conditions can also affect camera performance, because cameras have trouble seeing things on a messy or variable background, such as meat on a line. Figure 1 shows how a camera can more easily see objects when the background is plain.

Camera-based systems are ideal for assessing size and shape, such as with nuggets or patties.

Beyond the Visible Spectrum

Multi-spectral systems are different from camera-based systems. Instead of being limited to three colors, as in a camera-based system, multispectral systems are able to see between three and 15 spectral bands, and can see colors outside the visible spectrum. This enables them to see some chemical properties of the inspected object.

Multi-spectral systems were used in early space-based imaging to map landscape details on Earth. Detection in these systems is based on the materials the system expects to see. In the case of space-based imaging, the systems were set to detect water versus land versus vegetation. In food processing, these systems can be useful when contaminants are consistently made of the same materials; however, new or previously unknown contaminants will be missed, even if this “new” contaminant reappears multiple times.

Because these types of systems use a set number of spectral bands, they have a limited capacity to learn from what they see over time. And, like camera-based systems, multispectral systems aren’t able to assess quality measures.

From Multispectral to Hyperspectral

As the name suggests, hyperspectral systems collect information across the electromagnetic spectrum. They measure continuous bands through both the visible and invisible spectra, which means they see hundreds or thousands of essentially continuous light bands. This means that hyperspectral systems gather very robust data about the materials being inspected, down to a chemical level.

Hyperspectral imaging systems produce incredibly rich data on every piece of product they inspect. In a food processing plant, that means you can not only find, but identify, foreign materials based on their chemical signature, reducing your time to resolve issues by pointing the way to the likely source of the contaminant.

Hyperspectral imaging systems can go beyond just finding foreign materials. Unlike multispectral or camera-based systems, hyperspectral systems can assess quality measures such as steak tenderness or fat/lean ratios in sausages and can find myopathies like woody breast or spaghetti breast in poultry.

Hyperspectral systems are also exceptional in another way: These systems can use artificial intelligence (AI) to learn from the chemical data they collect over time. This makes these systems highly effective at identifying new or unexpected contaminants. It also means these systems can grow and change over time as the needs of a processing plant change, without the need for new capital equipment.

Recent advances in computing and computer processing have made it possible for these hyperspectral systems to operate on the line in real time.

How to Choose a Vision-Based System

Vision systems have tremendous advantages for food processing, but it’s important to know which system is the right one for your plant. Asking the right questions will help guide your selection process.

Figure 2: Examples of detection curves for a hyperspectral
imaging system.

First, ask to see a detection curve for the system. A detection curve, a chart that shows object size plotted against probability of detection, will give you a very clear indication of how successful a system will be in detecting objects of any size. Figure 2 shows examples of detection curves for different materials identified by a hyperspectral imaging system.