Two major applications of medical embedded systems are endoscopy and X-ray imaging, which in turn enhance diagnosis and treatment. Use of embedded vision within the medical imaging market is growing rapidly, driven by a call for minimally invasive diagnostic and therapeutic procedures, the need to accommodate aging populations, and rising medical costs.
To develop portable products for this market, developers often turn to third-party companies for help. Zibra Corp. turned to NET USA for assistance in the design of its coreVIEW series of borescopes and endoscopes. NET developed a remote camera with a 250 x 250 NanEye pixel imager from AWAIBA and a camera main board that incorporates an FPGA to perform color adjustment and dead pixel correction. An HDMI output on the controller board allows images captured by the camera to be displayed/viewed at distances of up to 25 feet.
Studying the skeletal changes of lizards posed an interesting problem for Yoel Stuart, then a graduate student at Harvard University. Stuart needed a portable X-ray system to use in the field. He worked with Rad-icon Imaging (now part of Teledyne DALSA) and Kodex to produce the final system. To create the system, Rad-icon developed the Remote RadEye200 with a 14-bit Shad-o-Box camera module that has a GigE Vision adapter with an Ethernet interface connected to a portable host PC. Kodex integrated this X-ray camera with a 50 kVp portable X-ray source from Source-Ray.
“While machine vision integrators can pay $10,000 for a system designed for industrial machine vision applications, to break into consumer markets, the vision system can't cost more than $500,” Basler's Lewerendt says. “New markets want vision without the PC, the GPU, or a hard drive. They want the system reduced to the minimum.”
Reducing the system cost, however, poses a conundrum for those companies traditionally involved in the machine vision market, where high-resolution, high-speed cameras can cost thousands of dollars. In a teardown of the iPhone X by iFixit, researchers identified that the TrueDepth sensor cluster used in the device costs Apple $16.70. Apple refused to comment on the price of these components, but such low costs are not unusual in high-volume consumer products.
While traditional machine vision camera vendors might not want to compete in the consumer market, there are other opportunities for vendors of smart camera modules. These include prosumer drones that can be used for industrial applications such as thermography to analyze the heat loss of buildings. Then there's BIKI from Robosea, an underwater drone created in the form of a fish, which employs a 3840 × 2160-pixel camera, 32 GB memory, and on-board features such as automated balance and obstacle avoidance.
As embedded vision proliferates in automobiles, medical imaging, remote inspection, and consumer electronics, opportunities will continue to increase for vision vendors, both traditional and nontraditional in scope.
Looking to the Future
Several underlying trends are prevalent in today's vision and camera markets. Embedded vision is changing the game in many ways. As we move forward, keep in mind:
Embedded vision systems are appearing in diverse products such as drones, automobiles, portable dental scanners, consumer robots, and virtual reality systems. These demand low-cost components to reach prosumer and consumer markets.
Companies that offer camera, lighting, and PC-based camera interface products for machine vision systems will find it difficult to compete. While lower-cost camera modules and camera interface/processing modules can be used for such applications, vendor margins will be substantially reduced, making it likely that only a handful of established machine vision vendors will enter the market.
Established software vendors will need to lower the cost of their products to compete in these markets due to the proliferation of easy-to-configure (although unsupported) open source code.
Cloud-based computing will negate the necessity for processing hardware currently used in embedded systems, where at present, deterministic and low-latency products may not be required, for example, those used in automatic number plate recognition.
In the future, we can expect even more trends to emerge. One thing is for sure, embedded technologies are making machine vision systems smart, intuitive, and connected to other parts of the factory, which is critical to maintain success in manufacturing today.