The Introduction of Embedded vision system and Embedded Camera Application
What is embedded vision?
The embedded vision system contains the entire signal chain from the light received by the selected imaging sensor to the output of the system. System output refers to processed or unprocessed images or information extracted from images and provided to downstream systems. Of course, the embedded system architect is responsible for ensuring end-to-end performance based on system requirements.
Embedded vision refers to a machine that uses visual methods to understand the surrounding environment. Embedded vision involves two technologies: embedded systems and computer vision (sometimes called machine vision). "Embedded vision" refers to an embedded system that extracts its meaning from visual input. Embedded system is any system based on micro-processing, it is not a general-purpose computer. Embedded systems are ubiquitous. They exist in countless places in automotive electronics, kitchen appliances, consumer electronics, and medical equipment. Computer vision is the use of digital processing and intelligent calculations to understand images and videos.
Embedded vision technology provides bright prospects for many exciting new applications to enter the market. These emerging applications include industrial display systems used in M2M applications, industry 4.0 solutions, advanced driver assistance systems (ADAS) and in-vehicle infotainment systems, digital SLR cameras, drones, robots, virtual reality (VR) systems, and medical equipment.
2, embedded single board
Embedded systems are more popular with SBCs, such as Raspberry Pi® or DragonBoard ® or Jetson ®. A stand-alone computer is a mini-computer with a recognized interface (USB, Ethernet, HDMI, etc.) that offers a range of features similar to traditional PCs or laptops.
3, Embedded processor
Among all embedded processing solutions, the most popular core option is the system on chip (SoC). This technology integrates one (or more) CPU (ARM), graphics processing unit (GPU), controller, other dedicated processors (DSP, ISP), and other components on a single chip.
SoC chips such as: Qualcomm's Snapdragon 820, 821, 835; Kirin 950 (4XCortex-A72+4XCortex-A53), 960 (4*Cortex-A73+4*Cortex-A53); MediaTek’s HelioX20 , X25, X30; the CPU architecture of these chips is based on the ARM architecture.
ARM cores such as: ARM7, ARM9, ARM11 (v6), to cortex-A7, A8, A9, A12, A15 (v7-A/R), to cortex-A53, A57 (V8), A72, A73. These cores are also known as the public version architecture, which is the general architecture launched by ARM
4, Embedded operating system
The x86 platform uses the classic CISC instruction set, the ARM platform uses the RISC instruction set, because of the economic benefits between Microsoft and Intel, at present only open source Linux can perfectly support the two instruction sets;
The rapid development of Linux has led to significant differences between the cores of adjacent versions (the fourth digit of the kernel version number). To this end, Linux developers in order to ensure the stability of the kernel, Linux loaded the driver into the kernel when the driver used a version verification mechanism. Therefore, a kernel version upgrade invalidates the linux driver and requires a recompilation drive based on the latest kernel version.
5, Features of Embedded Vision System
Compared with PC-based vision systems, the market demand for embedded vision systems is even larger:
The PC-based machine vision market is about to mature, and the embedded vision market is in the early stages of rapid growth:
The main advantages of embedded vision systems are:
Smaller size
Lower manufacturing costs
Less power consumption
More powerful
>> hardware acceleration (GPU, DSP,... ）
Widely used in embedded vision systems
The number of camera applications is growing in all industries today, and smarter applications are being implemented. Concepts like target recognition, depth perception, collision avoidance, and decision-making are reaching deep into the equipment used in our home life, urban environment, factories, and cars. Edge computing requires a variety of devices to work together for greater efficiency. The consumer electronics, industrial and automotive industries are looking at FPGAs to help build a flexible, intelligent learning environment that turns ideals into reality.
"Embedded vision can improve the safety and ease of use of cars in many ways. It plays a key role in certain applications, such as lane departure warning systems, collision warning systems, automatic parking systems, fatigue detection systems, "Blind Spot Detection System", "The entire embedded vision market in the automotive field has a very large amount of equipment and a very fast growth rate." [1] Successful cases of embedded vision technology in the automotive field include Mobileye's vision-based assisted driving system, iOnRoad assisted driving application and Roaddefend's vision driver fatigue warning system, etc. These systems can effectively reduce the probability of traffic accidents.
The remaining two major markets used by embedded vision are the traditional industrial vision application market and the security application market. For example, Israel's IOImage smart video security system is civilized in the industry with its high performance and ease of use. Another case is the swimming pool security system of MG International, which aims to prevent drowning in swimming through intelligent video surveillance. In the field of industrial vision applications, ABB, FUNUC (Japan), Mitsubishi, Kawasaki and other companies are in a leading position through the application of embedded vision systems to industrial robots.
Embedded vision Camera has been widely used in the military field, including vision guided missiles, unmanned reconnaissance aircraft, and military robots. In advanced electronic game console systems, such as Xbox, we can also find traces of embedded vision technology applications。
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