Getting Started with Hyperspectral Machine Vision Systems

How Hyperspectral Machine Vision Systems Improve Automated Inspection

By Mike Stebbins, COO & Optomechanical Engineer - January 10, 2024

What is Hyperspectral Machine Vision?

Hyperspectral machine vision combines hyperspectral imaging technology with automated inspectino systems to identify materials, detect defects, and improve quality control. Using hyperspectral cameras, imaging software, and advanced classification algorithms, manufacturers can automate inspections that are impossible with conventional vision systems.

Hyperspectral machine vision is useful in scenarios that call for automatic inspection, analysis, and action on the factory floor, such as automated sorting. Automated sorters typically use hyperspectral imaging to determine one of two types of information:

Classification: Category prediction (e.g., acceptable vs. not acceptable, product vs. foreign material, specific type of plastic, bruised vs. unbruised fruit).
Regression: Quantity prediction (e.g., ripeness, moisture content, percentage of a material in a mixed group, deviation from a nominal value such as product color).

Hyperspectral machine vision systems collect spectral data from the objects under inspection and then compute the classification or regression on a per-pixel or per-object basis. Depending on the application, the results might show information such as defects in the product, mixture ratios, or debris interspersed among good product.

The results obtained can be translated to meet the input requirements of the factory system, from triggering a subsequent process to alerting an operator to an issue. For example, the system can provide specific coordinates for a picking robot or air jet sorter that will separate the classified products.

Moreover, Resonon’s machine vision software is easy to use. See the video above for a short demonstration. The software’s algorithms learn to detect acceptable and out-of-spec products using training data. The training data are efficiently assembled, and multiple algorithms can be tested for optimal performance. By applying different models, you can determine a solution that meets your application-specific requirements.

How Shape, Texture, and Spectral Information Affect Hyperspectral Machine Vision Performance

Color difference is another way of saying spectral change. Spectral differences, even those too small to be detected by the human eye, are what hyperspectral imaging systems use to differentiate between objects. Object shape statistics (e.g. area, perimeter, orientation) can also be calculated with Resonon’s RVS software, but these statistics do not provide the primary information used by the hyperspectral machine vision system to make predictions.

Product spectral variation and texture generally create challenges for color-sensitive machine vision systems. A hyperspectral machine vision system can overcome this challenge by ensuring that the training data for the classification model include samples encompassing all the spectral variation and texture that the system will encounter in production. In some cases, product texture can even be an asset that improves classification accuracy in a hyperspectral system.

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Mike Stebbins

COO & Optomechanical Engineer

Mike Stebbins is the Chief Operating Officer and an Optomechanical Engineer at Resonon. He holds a Master’s degree in Mechanical Engineering and brings 20 years of diverse experience across aerospace structures, robotics, software, and hyperspectral systems to his role.

He enjoys seeing all the innovative ways customers use hyperspectral imaging to solve tough and important problems.

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