{"id":3697,"date":"2024-03-04T14:15:00","date_gmt":"2024-03-04T19:15:00","guid":{"rendered":"https:\/\/www.1stvision.com\/machine-vision-solutions\/?p=3697"},"modified":"2024-03-05T11:15:03","modified_gmt":"2024-03-05T16:15:03","slug":"webcam-vs-machine-vision-camera","status":"publish","type":"post","link":"https:\/\/www.1stvision.com\/machine-vision-solutions\/2024\/03\/webcam-vs-machine-vision-camera.html","title":{"rendered":"Webcam vs. machine vision camera"},"content":{"rendered":"\n

Webcams aren’t (yet) found in Cracker Jack boxes, but they are very inexpensive. And they seem to perform ok for Zoom meetings or rendering a decent image of an office interior. So why not just use a webcam<\/a> as the front end for a machine vision<\/a> application?<\/p>\n\n\n\n

Before we dig in to analysis and rationale, let’s motivate with the following side-by-side images of the same printed circuit board (PCB):<\/p>\n\n\n

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Machine vision camera and lens vs. webcam – Courtesy 1stVision<\/figcaption><\/figure>\n<\/div>\n\n\n

Side-by-side images<\/h2>\n\n\n\n

In the image pair above, the left image was generated with a 20MP machine vision camera and a high resolution lens. The right image used a webcam with a consumer sensor and optics.<\/p>\n\n\n\n

Both were used under identical lighting, and optimally positioned within their specified operating conditions, etc. In other words we tried to give the webcam a fair chance.<\/p>\n\n\n\n

Even in the above image, the left image looks crisp with good contrast, while the right image has poor contrast – that’s clear even at a wide field of view (FOV). But let’s zoom in:<\/p>\n\n\n

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Clearly readable labeling and contact points (left) vs. poor contrast and fuzzy edges (right)<\/figcaption><\/figure>\n<\/div>\n\n\n

Which image would you prefer to pass to your machine vision software for processing? Exactly. <\/p>\n\n\n

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Machine vision cameras with lens mounts that accept lenses for different applications<\/figcaption><\/figure>\n<\/div>\n\n\n

Why is there such a big difference in performance<\/h2>\n\n\n\n

We’re all so used to smartphones that take (seemingly) good images, and webcams that support our Zoom and Teams meetings, that we may have developed a bias towards thinking cameras have become both inexpensive and really good. It’s true that all cameras continue to trend less expensive over time, per megapixel delivered – just as with Moore’s law<\/a> in computing power.<\/p>\n\n\n\n

As for the seemingly-good perception, if the images above haven’t convinced you, it’s important to note that:<\/p>\n\n\n\n

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  1. Most webcam and smartphone images are wide angle large field of view (FOV)<\/li>\n\n\n\n
  2. Firmware algorithms may smooth values among adjacent pixels to render “pleasing” images or speed up performance<\/li>\n<\/ol>\n\n\n\n

    Most machine vision applications, on the other hand, demand precise details – so firmware-smoothed regions may look nice on a Zoom call but could totally miss the defect-discovery which might be the goal of your application! <\/p>\n\n\n\n

    Software<\/h2>\n\n\n\n

    Finally, software (or the lack thereof) is at least as important as image quality due to lens and sensor considerations. With a webcam, one just gets an image burped out, but nothing more.<\/p>\n\n\n\n

    Conversely, with a machine vision camera, not only is the camera image better, but one gets a software development kit<\/a> (SDK). With the SDK, one can:<\/p>\n\n\n\n