Embedded Technology
Embedded technology in machine vision applications
Peter Marchant, embedded division manager at Review Display Systems, discusses the needs and requirements of embedded computing in a machine vision application
A
cross many industries the use of embedded computing technology is becoming more wide-ranging. When
compared with traditional PC hardware, today’s embedded computing systems are powerful, energy efficient, mechanically compact and can provide an effective, inexpensive computing solution.
Embedded computing platforms Intel, AMD, and ARM processors are widely used across an extensive range of embedded computing platforms. Processing performance, memory bandwidth, expansion capability and energy efficiency are key factors that determine the suitability of an embedded computer for a real-world application.
Migrating from PC hardware to a single board computer (SBC), Computer- on-Module (COM) or System-on-Module (SOM), will invariably enable a smaller, more power efficient, cost-effective embedded system to be created. Additional memory, add-on peripherals and expansion boards can provide enhanced performance, functionality and connectivity as required.
What is machine vision? Machine vision, which is much more than the current on-trend development of autonomous vehicles, is one of the fastest-growing application sectors across a diverse range of manufacturing, control and service industries that is helping to improve productivity, develop functionality and enhance reliability.
Machine vision systems employ high performance computing and image technology coupled with software algorithms to enable automatic image- based recognition, inspection, and analysis for applications such as process control, factory automation, food processing, access control, robot guidance, quality assurance and security systems to name but a few.
24 March 2022
Machine vision in vehicle monitoring
An application where machine vision is actively employed is in vehicle monitoring systems where Automatic Number Plate Recognition (ANPR) systems are used to control vehicle access, provide traffic control, enable site security, or monitor complex multi-lane, national motorways.
Vehicle tracking
To perform intelligent vehicle tracking across the full range of view of an image sensor, tracking systems must be capable of high-speed number plate recognition in fast-moving, high-density traffic environments. Image processing algorithms need to be able to locate and recognise multiple number plates in the image sensors field of vision, read, acquire, and process image data in real time with a high degree of accuracy and repeatability.
Components in Electronics
Image recognition
Machine vision-based vehicle monitoring systems need to be able to react, respond and execute image recognition algorithms with speed and precision to determine: 1. a vehicle is identified, 2. identification of a number plate, 3. character and symbol recognition, and 4. perform follow-up data analysis with the image data obtained.
Essential hardware requirements A typical machine vision system for a vehicle monitoring application will require the integration of a camera/image sensor, high-performance embedded computing hardware, and suitable power management.
Image sensors
Image sensors are now able to support multiple resolutions and enhanced frame rates while still providing exceptional
image quality and uniformity. Sensors are now delivering resolutions of 8K (8190 x 5460 pixels) with frames rates of 60fps (frames per second) and above.
Embedded computing hardware for machine vision
Of the many embedded SBCs now available, the Pico-ITX board provides an ideal hardware solution for machine vision applications in quality control, security, and transportation.
The most recent Pico-ITX boards now feature the latest 11th Generation (Tiger Lake) Intel Core i3, i5, i7, Celeron SoC (System-on-a-Chip) processors. 11th Generation processors are now manufactured using a 10nm process which has resulted in a 15-20 per cent performance improvement over the 14nm process of previous ninth generation processors.
www.cieonline.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54
