With the emergence of advanced robotics and machine learning technologies and the transition to the Industry 4.0 manufacturing model, the field of embedded
machine vision
applications is gradually expanding. Vision applications are finding a place in a growing number of emerging industrial, automotive and consumer electronics applications.
The rapid development of electronic products used in modern vehicles, especially advanced driver assistance systems (ADAS) and in-vehicle infotainment systems, has also brought opportunities for embedded video applications. Developers of consumer electronics solutions such as drones, gaming systems, surveillance and security see the benefits of embedded vision technology.
Many key components and tools that are critical to the rapid deployment of low-cost embedded vision solutions are finally available. Today, design engineers can choose from a variety of lower-cost processors that offer small size, high performance, and low power consumption. At the same time, thanks to the fast-growing mobile market, design engineers are able to benefit from the proliferation of cameras and sensors. At the same time, improvements in software and hardware tools help simplify development and speed time-to-market.
The following embedded vision technology solution provider Long Ruizhike (www.loongv.com) will discuss how to use embedded vision technology and the reasons for using embedded vision technology.
Embedded vision systems actually cover any device or system that executes image signal processing algorithms or vision system control software. The key components in an intelligent vision system are high-performance computing engines for real-time high-definition digital video streaming, large-capacity solid-state storage, smart cameras or sensors, and advanced analytics algorithms. Processors in these systems can perform image acquisition, lens correction, image preprocessing and segmentation, object analysis, and various heuristics functions. Embedded vision system designers employ a variety of processors, including general-purpose CPUs, graphics processing units (GPUs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), and application-specific standard products designed for vision applications (ASSP). The above processor architecture has obvious advantages and disadvantages. In many cases, design engineers consolidate multiple processors into a heterogeneous computing environment. Sometimes the processor is integrated into a component. Additionally, some processors use specialized hardware to achieve the highest possible performance of vision algorithms. Programmable platforms such as FPGAs provide design engineers with a highly parallel compute-intensive application architecture and resources for other applications such as I/O expansion.
On the camera side, embedded vision system design engineers use analog cameras and digital image sensors. Digital image sensors are typically CCD or CMOS sensor arrays that require a visible light environment. Embedded vision systems can also be used to sense other data such as infrared, ultrasonic, radar, and lidar.
More and more design engineers are turning to "smart cameras" that employ cameras or various sensors as the core of all edge electronics in vision systems. Other systems transmit sensor data to the cloud to reduce the load on the system processor, in the process minimizing system power, footprint and cost. However, this approach faces problems when critical decisions need to be made with low latency based on image sensor data.
Three recent trends in the market promise to revolutionize the face of embedded vision systems. First, the rapid development of the mobile market has provided embedded vision design engineers with a large number of processor options that can provide relatively high performance with low power consumption.
Second, the Mobile Industry Processor Interface (MIPI) from the MIPI Alliance offers design engineers an alternative to building innovative and cost-effective embedded vision solutions using standards-compliant hardware and software components. Finally, the proliferation of low-cost sensors and cameras for mobile applications has helped embedded vision system design engineers achieve better solutions and lower costs.
Although embedded vision solutions have been on the market for many years, the speed at which this technology can evolve has been limited by a number of factors. The key elements of embedded vision have not yet been implemented in a cost-effective manner. In particular, computing engines capable of processing high-definition digital video streams in real time are not yet widespread. The limitations of high-capacity solid-state storage and advanced analytics algorithms also present challenges.
Embedded vision technology is also largely limited by component performance. Many of the key components in an intelligent vision solution, especially the computing engines needed to process high-definition digital video in real time, are prohibitively expensive.
With the development of mobile processors, the emergence of low-power FPGAs and ASSPs, the promotion of MIPI interface standards, and the proliferation of low-cost cameras and sensors, design engineers can apply once highly specialized technologies to smart factory automation, automotive electronics and consumer electronics field. Obviously embedded vision technology will play a very important role here.
Many key components and tools that are critical to the rapid deployment of low-cost embedded vision solutions are finally available. Today, design engineers can choose from a variety of lower-cost processors that offer small size, high performance, and low power consumption. At the same time, thanks to the fast-growing mobile market, design engineers are able to benefit from the proliferation of cameras and sensors. At the same time, improvements in software and hardware tools help simplify development and speed time-to-market.
The following embedded vision technology solution provider Long Ruizhike (www.loongv.com) will discuss how to use embedded vision technology and the reasons for using embedded vision technology.
Embedded vision systems actually cover any device or system that executes image signal processing algorithms or vision system control software. The key components in an intelligent vision system are high-performance computing engines for real-time high-definition digital video streaming, large-capacity solid-state storage, smart cameras or sensors, and advanced analytics algorithms. Processors in these systems can perform image acquisition, lens correction, image preprocessing and segmentation, object analysis, and various heuristics functions. Embedded vision system designers employ a variety of processors, including general-purpose CPUs, graphics processing units (GPUs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), and application-specific standard products designed for vision applications (ASSP). The above processor architecture has obvious advantages and disadvantages. In many cases, design engineers consolidate multiple processors into a heterogeneous computing environment. Sometimes the processor is integrated into a component. Additionally, some processors use specialized hardware to achieve the highest possible performance of vision algorithms. Programmable platforms such as FPGAs provide design engineers with a highly parallel compute-intensive application architecture and resources for other applications such as I/O expansion.
On the camera side, embedded vision system design engineers use analog cameras and digital image sensors. Digital image sensors are typically CCD or CMOS sensor arrays that require a visible light environment. Embedded vision systems can also be used to sense other data such as infrared, ultrasonic, radar, and lidar.
More and more design engineers are turning to "smart cameras" that employ cameras or various sensors as the core of all edge electronics in vision systems. Other systems transmit sensor data to the cloud to reduce the load on the system processor, in the process minimizing system power, footprint and cost. However, this approach faces problems when critical decisions need to be made with low latency based on image sensor data.
Three recent trends in the market promise to revolutionize the face of embedded vision systems. First, the rapid development of the mobile market has provided embedded vision design engineers with a large number of processor options that can provide relatively high performance with low power consumption.
Second, the Mobile Industry Processor Interface (MIPI) from the MIPI Alliance offers design engineers an alternative to building innovative and cost-effective embedded vision solutions using standards-compliant hardware and software components. Finally, the proliferation of low-cost sensors and cameras for mobile applications has helped embedded vision system design engineers achieve better solutions and lower costs.
Although embedded vision solutions have been on the market for many years, the speed at which this technology can evolve has been limited by a number of factors. The key elements of embedded vision have not yet been implemented in a cost-effective manner. In particular, computing engines capable of processing high-definition digital video streams in real time are not yet widespread. The limitations of high-capacity solid-state storage and advanced analytics algorithms also present challenges.
Embedded vision technology is also largely limited by component performance. Many of the key components in an intelligent vision solution, especially the computing engines needed to process high-definition digital video in real time, are prohibitively expensive.
With the development of mobile processors, the emergence of low-power FPGAs and ASSPs, the promotion of MIPI interface standards, and the proliferation of low-cost cameras and sensors, design engineers can apply once highly specialized technologies to smart factory automation, automotive electronics and consumer electronics field. Obviously embedded vision technology will play a very important role here.