Machine Vision Is A Relatively New, Complex, And Evolving Field Of Artificial Intelligence. And Machine Vision Should Be Described As The Use Of Sensors To Receive Signals That Make Up An Image Of An Object And Are Used By A Computer Or Other Signal Processing Equipment To Interpret And Analyze Signals Received From The Sensor.
Machine vision is classified into three subcategories: Stereo Correspondence, Scene Reconstruction, and Object Recognition.
A brief history of car vision
Until a few decades ago, most people believed that machine vision was limited to science fiction. Over the past decade, however, machine vision has become one of the growing areas of artificial intelligence. Infrastructural research conducted by leading scientists in the field of machine vision has made this technology tangible in important fields such as robotics. Why is car vision technology important?
Machine vision has attracted the attention of commercial and industrial companies because it allows computer programs to perform their tasks and applications automatically, tasks that previously required a Human Supervisor to perform them. Of course, programs are written to control industrial tools and equipment.
Machine vision is used as an engineering tool in digital tools and in computer networks to control other industrial tools such as controlling robotic arms or removing defective equipment.
Accordingly, machine vision should be described as a branch of engineering that interacts with the disciplines of computer science, optics, mechanical engineering, and industrial automation.
One of the most widely used applications of this technology is in the inspection of industrial goods such as semiconductors.
Like the workforce that inspects goods on the production line with the naked eye to determine the quality and type of construction, machine vision uses digital and smart cameras and image processing software to do so.
In this regard, devices equipped with machine vision are used to perform special tasks such as counting objects, reading the serial number of goods, and searching for defective goods.
The business world is now making extensive use of machine vision-based systems to identify objects and implement intelligent robotic arms that are supposed to operate 24 hours a day and perform high-volume calculations.
The human factor is indeed better at detecting errors in the short term, but due to the many features that machine vision has, in the long run, devices equipped with this technology will replace humans because in difficult and high-pressure conditions the error rate of smart devices Less than humans.
Tasks such as Assembly Line Part Recognition, Face Recognition, and operational applications such as Unmanned Aerial Vehicles and Unmanned Automobiles are among the maps defined for vision systems. They are cars.
What creative applications can scientists define for machine vision?
The current goal of machine vision systems is to implement a general framework for solving unsolved problems and to further implement an operational and applied system in the field of robotics. However, achieving these goals is not an easy task. One of the major challenges in implementing these systems is the lack of a comprehensive process for constructing three-dimensional object recognition components.
Today, machine vision in systems based on this technology is divided into two groups: active vision (Active Vision) and passive vision (Passive Vision). In the field of active vision, the system interacts directly with the environment and receives environmental information.
Solutions such as SONAR, LIDAR, and RADAR emit audio, light, or radio signals that attempt to receive and model a particular image pattern by listening to the reflected signal.
If you have read the online machine learning article, you have probably guessed that your active vision falls into the category of online machine learning because the information is received and processed in an instant.
The second approach, called passive vision, is based on the mechanism that receives light from the operating environment (similar to what the human eye does) and creates an image of an object without creating abnormal waves in the environment.
In addition, access to the hardware required to implement this system model is more cost-effective than the first model.
The second approach, called passive vision, is based on the mechanism that receives light from the operating environment (similar to what the human eye does) and creates an image of an object without creating abnormal waves in the environment.
In addition, access to the hardware required to implement this system model is more cost-effective than the first model. The second approach, called passive vision, is based on the mechanism that receives light from the operating environment (similar to what the human eye does) and creates an image of an object without creating abnormal waves in the environment.
In addition, access to the hardware required to implement this system model is more cost-effective than the first model.
The important thing to note is that systems based on machine vision view object differently from humans.
In situations where humans can rely on inferences and hypotheses, intelligent equipment must create a visual image of an object or human by testing and analyzing individual pixels and trying to draw conclusions based on information support and methods such as pattern recognition.
Of course, some machine vision algorithms are somewhat closer to biological examples by mimicking human vision, but limited algorithms have been developed to effectively analyze and identify image-related features. All in all, the main purpose of machine vision systems is to implement automatic Scene Reconstruction and Object Recognition systems.
What is the difference between car vision and computer vision?
Some people believe that machine vision and computer vision are two synonymous terms, while technically the two concepts are very different from each other. Normally, machine vision systems can consistently analyze images, but computer-based image processing (computer vision) is generally designed to perform repetitive tasks. To be more precise, computer vision refers to the process of automating the reception and analysis of images.
The focus of computer vision systems is more on the ability to analyze images, extract important information from them, and understand the objects or entities within images.
In machine vision, systems try to use computer vision techniques in industrial and practical applications.
Despite advances in both areas, no machine vision or computer vision system can compete with some of the biological features of the human eye in terms of image perception, light tolerance, image enhancement, and other visual aspects.
In the last few years, the line between machine and computer vision systems has blurred, and the two technologies are gradually merging. This is why the term machine vision is used today in industrial and non-industrial environments such as biomedicine. In addition, the term machine vision is used when describing the capabilities of search engines and image-based recognition services in searches.
This is why the term machine vision is used today in industrial and non-industrial environments such as biomedicine. In addition, the term machine vision is used when describing the capabilities of search engines and image-based recognition services in searches.
This is why the term machine vision is used today in industrial and non-industrial environments such as biomedicine. In addition, the term machine vision is used when describing the capabilities of search engines and image-based recognition services in searches.
What are the components of a car vision system?
Typically, the basic components needed to develop computer vision systems and machine vision are similar.
A system based on machine vision is made of the following components:
- Imaging or receiving device: This device can be one or more digital cameras equipped with an image sensor and a lens with optical properties suitable for taking photos.
- The interface that prepares images for processing: This component is needed when an analog camera is used to capture images, and it must use an interface such as a card that is used to receive and send a video signal to a computer.
- A computer system or processor (built-in) that is responsible for processing signals: Because today’s imaging systems, such as smart cameras, are equipped with dedicated processors, the process of processing and analyzing images takes place inside the imaging device and there is little need for this system.
- Machine vision software (image processing program): A program whose job is to provide an image or information that is structured and understood by humans. Typically, these programs are written in Python or MATLAB.
- Input / Output Hardware: Tools such as network cards are used to send or receive reports prepared for the relevant units.
- Appropriate light sources and conditions: Provide the possibility of interacting with the operating environment to receive images from the environment, analyze the received images, and produce the desired outputs.
- Synchronous sensor: The above sensor is used to identify components such as an optical sensor or magnetic sensor. The synchronization sensor determines when a component is in the right place. For example, a car must maintain a safe distance from the car in front.
Methods of data processing by machine vision algorithms
Like other algorithms in the world of artificial intelligence and machine learning, the world of machine vision uses a variety of methods to process the information received.
These methods include the following:
- Pixel count: In this method, the number of light and dark pixels is counted.
- Set a threshold: Convert a photo with gray areas to a black-and-white photo by placing lighter pixels toward white and darker pixels toward black by setting a threshold.
- Segmentation: Convert the input image into different sections for positioning and counting pixels.
- Stain Detection and Manipulation: Examine an image to find breakpoints among pixels. These spots are used as a special mark on the photo.
- Recognition by existing components: Extract specific components from an input image.
- Pattern Detection Identification of Resistance: The position of an object that may be rotated, resized, or partially covered by another object is identified.
- Barcode reading: Identify and determine one-dimensional and two-dimensional codes scanned by machines.
- Optical Character Recognition: Automatically reading a text.
- Measurement: Measuring the dimensions of an object (in millimeters or inches).
- Edge Identification: This process refers to finding the edges of an object in an image. Edges are the recognition of a variety of curved and smooth lines in different parts of a photo, some of which may be long and long and some short.
- Pattern recognition by matching, finding, matching, and counting specific shapes in an image.
In most cases, a machine vision system uses a combination of the above methods to process data to fully examine an image.
What skills does a car vision specialist need?
If you are interested in reading this article, you should enter the world of car vision professionally, you should know what skills a car vision specialist needs in general.
Companies require different skills from individuals depending on the type of system they plan to implement or complete, however, as a car vision specialist you should generally have the following skills:
General skills
- Sufficient knowledge of the basic topics of artificial intelligence, common patterns of machine learning (with the observer and without observer), and deep learning.
- Sufficient knowledge of using TensorFlow, PyTorch, and Scikit-learn frameworks.
- Fluency in a powerful programming language such as Python, R, or MATLAB.
- Familiarity with the concepts of object-oriented programming.
- Familiarity with the gate to interact with other development teams and relative familiarity with the Linux operating system.
- Ability to solve problems and come up with creative solutions.
- Ability to work in a team.
Machine vision skills
- Familiarity with the basics of image and video processing.
- Familiarity with the OpenCV library.
- Familiarity with the concepts of face recognition and recognition, object and activity recognition, text recognition.
- Speech processing, which includes familiarity with the basics of voice and speech processing.
- Familiarity with speech recognition methods.
- Familiarity with voice authentication.
- Familiarity with how to use deep learning in the field of sound.
Data mining-related skills
- Introduction to reinforcement learning.
- Familiarity with bidder systems.
- Familiarity with statistics and statistical tests.
- Familiarity with the concepts of big data processing.
- Familiarity with the concepts of data visualization and the production of appropriate reports.
In addition to the skills mentioned, some companies expect experts to master machine images and vision processing algorithms such as object tracking, background removal and perspective correction, master machine learning frameworks such as a cross, and master the Res, Faster network architecture.
Know Rcnn, Inception, YOLO Algorithm, and oppose.
How Much Money Does a Car Visionist Make?
Usually, the most important motivation to enter the world of car vision is the amount of salary that people receive. The amount you receive depends on the company you intend to join. For example, an experienced car vision specialist with several years of professional experience and successful projects at a company like Facebook earns $ 140,000 a year, yet their average salary is $ 89,000 a year.
In Iran, companies use the same agreement stereotypes, however, if you are planning to be hired as a car vision specialist in the company, the minimum offer you have to make is 8 million Tomans (if practical work experience or research article published in You do not have ISI journals.). If you have experience in this field, the minimum offer you should submit should not be less than 12 million Tomans.
Applications of machine vision
The vision of the technology machine is evolving and is expected to enter various industries and fields in the coming years. The most important applications of machine vision that provide a good job market for professionals are the following:
Industrial automation
- Implementation of safety systems to be used in industrial environments. Investigating raw materials for production, inventory control, and management systems (counting, barcode reading, and data storage in digital systems) and implementation of linear pattern tracking robots used to transport cargo in industrial plants are important areas related to industrial automation. In addition, machine vision systems are widely used in the semiconductor manufacturing industry. Without these systems, the production of computer equipment is slow.
Plaque reader
- License plate number recognition is one of the most widely used applications of car vision used today by institutions such as traffic police. The above technology allows car license plates to be read correctly. These systems can be used in smart parking lots, entrances, and exits of organizations and large complexes to control traffic. In addition, this technology has many applications on the roads.
Speedometer
- Using the image of two cameras, the depth of the image can be obtained, and thus the speed of a machine can be calculated. The advantage of these systems over laser samples is that they are passive. Passive in the sense that these devices do not emit waves, and therefore the use of a disruptor or tracker to prevent the recording of violations is ineffective.
Registration of violations within the city
- Image processing of cameras installed at intersections allows to obtain the time, speed, direction of movement, and license plates of cars and crossing the red light, stopping on the pedestrian lane, turning left and right, and exceeding the speed limit when crossing Recorded the intersection.
last word
Machine vision systems, based on modifying and optimizing existing solutions or combining available methods with other techniques, make it possible to automate some complex or tedious tasks for humans.
Of course, scientists are looking into whether it is possible to use a large number of low-quality cameras instead of expensive cameras to match images and evaluate their performance with systems that use dual-quality cameras.
In addition, scientists are looking to make better use of the GPU to perform machine vision processes, so it is expected that 3D pixels will be used more accurately in this area in the future, providing access to more detail. All in all, investing in machine vision learning creates good job opportunities for you.