FPGA-based embedded vision addresses the growing need for high-speed imaging solutions to enable edge intelligence in low-power.
FREMONT, CA: Cameras and other equipment used in video surveillance and machine vision perform a variety of tasks including, image signal processing, video transport, format conversion, compression, and analytics. Because of various technological advancements in camera sensors, the trend to replace analog cameras with smart internet cameras, and the progress of artificial learning-based video analytics, FPGAs exceed many of the critical requirements needed for vision-based systems.
Embedded vision uses a combination of high-speed cameras and computers to perform complex inspection tasks in addition to digital image acquisition and analysis. One can use the resulting data for pattern recognition, object sorting, robotic arm control, and more. FPGAs are ideal for embedded vision cameras, allowing designs to accommodate a wide variety of image sensors and embedded vision-specific interfaces. FPGAs can also be helpful in vision processing accelerators inside the edge computing platform to leverage the power of artificial intelligence for analysis of the embedded vision data. FPGAs integrate real-time functions into the camera system for pixel-oriented gain control, compensation of defective pixels, increased dynamic range, and more.
According to Yahoo Finance, FPGAs also provide an open, high-performance, scalable framework for image streaming and device control over the network. This interface standard offers an environment for networked embedded vision systems based on server architectures, allowing users to connect multiple cameras to computers. With FPGA, users can reduce the board size and component count of embedded vision systems and minimize hardware re-spins, to get the product to market faster, and keep the product in the market for a much longer time.
As more vision systems that have progressive technologies reach the market, vision system designers need to understand the benefits and trade-offs of using FPGAs. Applying these FPGA development methods could help them achieve enhanced vision processing.
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