Cameras typically take up the lion share of revenue resources in a machine vision system; however, today’s cameras are faster, smaller, lighter, smarter and less expensive. A good quality GigE monochrome camera should cost an end user around 1K, costs will go up as megapixel size increases.
When choosing a camera interface keep these points in mind:
- GigE Vision: Used in low & mid-end vision systems with less critical speed and timing demands; cost effective solution when full speed is not required.
- Camera Link: Industry default choice for higher speed connectivity where limited cable length and high cable costs are acceptable, frame grabber is required
- CoaXPress: Newer technology, for applications that require higher speeds, longer cable lengths, a frame grabber is required.
- Camera Link HS: Originally designed to overcome the speed limitations of Camera Link for line scan cameras, frame grabber will be required
- GigE Vision over 10 GigE: Built on GigE Vision, faster physical layer and better timing accuracy with much higher power consumption; requires server grade equipment for implementation.
Depending on the camera and the method in which you wish to obtain or retain the data from the image, a Frame Grabber may be required (see camera interface information above). Frame Grabbers come in a variety of configurations; choices include; bandwidth and DDR-RAM size, on-board camera controls, the number of cameras that each board controls and the newest innovation adds FPGA programing on the board. A basic frame grabber will cost around $500.00, a frame grabber with FPGA programming capability will run around $2,500 or more depending on the configuration of the board, but keep in mind, the board with FPGA will allow you to reduce your CPU/GPU requirements as your images will now be preprocessed. Using an FPGA enabled frame grabber will likely reduce costs for computer related hardware as your bandwidth and RAM requirements will be much less than with a traditional Frame Grabber.
Depending on your application, lighting can be a strong contender for vision system dollars. Good lighting is essential to the success of a machine vision system. Most vision applications will benefit from line lights or back lights. Getting the lighting right will save you development dollars. A good quality 5” line light or backlight can run $450-$850 depending intensity controls and size. Ask your machine vision expert of integrator about how best to “highlight” your intended object.
Lenses typically represent the lowest percent of dollars spent on a machine vision system. A standard machine vision 16mm lens typically runs $100-$200, but a high quality specialty lens, such as motorized or Near IR corrected can run well over $1,000.00. Be sure to invest in a good quality lens.
Software can be free when the SDK (software development Kit) from the camera manufacturer is included in the camera purchase or can run thousands when complex licensing schema or specialty vision software is required. There are many machine vision related software solutions on the market, make sure your machine vision professional explains what the free SDK can do for you before investing in costly software.
Cables represent the least costly part of a vision system but be sure to get certified cables from a reputable cable manufacturer and ensure your integrator is not cutting corners by using cables made by non-certified manufacturers. A bad cable can make a great vision system worthless.
Imaging is now touching every industry, entertainment, marketing, auto, aerospace, packaging etc. Just think of all those robots being developed! The newest technology is the “Smart” camera. These cameras have embedded FPGA software right on the board and can be addressed using flow chart style programming. No longer will vision engineers need an FPGA programmer to complete the system, with a little training a vision engineer will be able to program camera controls, enhance images and much more, all from their desk top or mobile device. It is truly an exciting time to be involved in machine and embedded vision.
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