Version 2.0 of the GigE Vision standard enables video transport over 10GigE. As 10GigE Vision cameras become available, they are bundled with off-the-shelf 10G Network Interface Cards (NIC) serving as their frame grabber. This has the benefit of not requiring specialized frame-grabbing hardware but creates an I/O bottleneck between the NIC and the host CPU as NICs are unable to compress or pre-process the video stream in hardware. It gets worse when multiple 10G video channels are required.
The benefit of FPGA for handling Gigabytes of I/O throughput while increasing system performance and reducing system latency with on-the-fly processing is undeniable, but designing FPGAs traditionally requires specialized hardware skills and long design cycles. Quickplay is a software-defined development environment which enables developers with no FPGA skills to build FPGA-accelerated systems, including intelligent vision systems. To complement this development platform, QuickStore is an online marketplace where developers shop for IP that they can use seamlessly in QuickPlay to build their FPGA-accelerated applications. Assembling a 10GigE Vision frame grabber with embedded pre-processing is a straightforward process in the Quickplay IDE. As illustrated in Figure 1, the user drag &drops required processing blocks (IP) from the built-in library, from the catalog of QuickStore IP, or inserts his own processing kernels in C or Verilog/VHDL, and creates a dataflow representation of the FPGA design. This is accomplished graphically or in C++, as preferred. The dataflow modeling is based on a streaming architecture, perfect for real-time video processing. This specification of the hardware design is done at the highest level of abstraction, without reference to any hardware element such as clocks, resets, busses and wires, FIFOs and DMA engines, etc. In figure 1 example, a 10GigE Vision intelligent frame grabber is created by inferring one GigE Vision 2.0 controller IP and associated ports for data and control (including GVSP and GVCP), a 2:1 packet splitter, a JPEG 2000 compression IP and its memory buffer, and a Sobel edge detection filter kernel developed in C. Both the compressed video and Sobel-processed video are pushed out using two separate output ports. This is just one example of an intelligent frame grabber that performs in parallel on the 10G video stream JPEG 2000 compression as well as contour detection, however the ability to insert custom processing kernels in C or in Verilog/VHDL and IP from Quickstore provide endless possibilities. The second step in the development process is the validation of the dataflow model created in Quickplay, using Linux native gcc compiler and gdb debugger. Validation requires linking the design model to either a unitary test application or to the final application (i.e. the frame grabber application software), using the QuickPlay API. Figure 2 presents a subset of the API and illustrates the communication between software application and FPGA design. The third step is the Build stage where the software model of the frame grabber is compiled into a hardware (i.e. HDL) representation. This step requires the user to specify:
The Implement stage is the fourth and final step. Quickplay invokes the FPGA vendor’s tool suite completely in the background, until the generation of the FPGA bitstream which can be loaded onto the target board. Executing once again the software application used in step two now communicates with the FPGA hardware and produce the same output, albeit at a much faster pace. The user may at any time customize or completely re-architect the design, by replacing, modifying or adding processing elements, changing the physical I/O interfaces, and even selecting a different FPGA platform.
The ability to seamlessly integrate and interconnect IP from QuickStore, IP designed in-house whether in Verilog/VHDL or in C, and built-in elements from the Quickplay library, provides computer vision professionals an easy and unique way to build FPGA-augmented applications, be it intelligent frame grabbers or other smart video and image processing adapters, all without hardware or FPGA expertise.
Dank Gesichtserkennungstechnologie identifiziert eine Security-Kamera Personen und sendet deren Namen an das Smartphone des Besitzers bzw. informiert den Nutzer über unbekannte Gesichter im Haus.
Conventional cameras capture images using only three frequency bands (red, blue, green), while the full visual spectrum is a much richer representation that facilitates a wide range of additional and important applications. A new technology allows conventional cameras to increase their spectral resolution, capturing information over a wide range of wavelengths without the need for specialized equipment or controlled lighting.
Although different 3D cameras and scanners have existed for some time, present solutions have been limited by several unwanted compromises. If you wanted high speed, you would get very low resolution and accuracy (e.g. Time-of-Flight cameras and existing stereo vision cameras, which despite being fast typically have resolution in the millimeter to centimeter range). If you wanted high resolution and accuracy, you would typically get a camera that was slow and expensive (e.g. the high accuracy scanners).
Vom 18. bis 19. Oktober veranstaltet der VDI die nunmehr 4. Fachkonferenz zum Thema ‚Industrielle Bildverarbeitung‘ im Kongresshaus Baden-Baden. In 19 Fachvorträgen werden u.a. die Schwerpunktthemen Automation in der Robotik mit 3D-Bildverarbeitung, Oberflächeninspektion und Bildverarbeitung in der Nahrungsmittelindustrie und intelligenten Logistik behandelt.
C-Red 2 is an ultra high speed low noise camera designed for high resolution SWIR-imaging based on the Snake detector from Sofradir. The camera is capable of unprecedented performances up to 400fps with a read out noise below 30 electrons. To achieve these performances, it integrates a 640×512 InGaAs PIN Photodiode detector with 15m pixel pitch for high resolution, which embeds an electronic shutter with integration pulses shorter than 1μs. The camera is capable of windowing and multiple ROI, allowing faster image rate while maintaining a very low noise.
Ob Automatisierung, Mensch-Maschine-Kollaboration in der Robotik oder selbstfahrende Autos – die Auswahl des richtigen Sensors hängt stark von der Applikation und dem gewünschten Output ab. Diese 6 Faktoren helfen Ihnen dabei, den passenden Sensor für Ihre Applikation zu finden!