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Key considerations when selecting a lens for machine vision

One of the most common mistakes is that engineers /scientist will often choose a camera and a sensor before choosing a lens. This could lead to a situation where there isn’t an optimised lens available for the application.

Remember all machine vision systems require production of an image of sufficient quality to enable the required measurements to be made. The critical determinant of image quality will be your choice of lens or lens combination. In a machine vision system, your choice of lens can also impact other factors including measurement accuracy, achievable speed, and the reproducibility and reliability of the downstream analysis.

A typical machine vision lens selection process should involve identifying customer needs and making sure they understand key specifications. The key specifications are desired field of view, resolution (or size of the smallest detail or part to see or identify), working distance / range and depth of field (the height of the objects under inspection). Once these four key specifications are known, it is much easier to select an optimised lens and camera.

There is a direct relationship between the most suitable lens and the sensor used in the camera. Knowing the sensor size, select a lens that is able to illuminate the complete sensor area in order to avoid shading and vignetting. Your chosen lens also has to be able also to resolve the sensor pixel size. The better the optical resolution of your lens, the better detailed machine vision analysis can be achieved.

Standard resolution lenses (MTF 70 to 90 lp/mm ; low distortion and vignetting) are the most widely used lens type for machine vision sensors with a resolution of less 1 megapixel. High resolution lenses (MTF > 120 lp/mm and very low distortion) offer improved imaging performance compared to standard lenses and are particularly well suited to cameras with a small pixel size.

This short feature was intended to give you an overview of the many considerations when looking to develop an optimised inspection solution using machine vision.

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