Data-driven trade procurement cycles

List of the table of contents of this article:

• 1, What is image recognition and understanding?
• 2, What are the typical applications of image recognition in intelligent networked cars?
• 3. What deployment modes are generally included in the deployment of intelligent image recognition in the video surveillance system, and its advantages, disadvantages and suitability...
• 4 、 Specific applications of image recognition

What is image recognition and understanding?

Image recognition is a popular research direction in the field of artificial intelligence. It allows computers to independently analyze, process and identify digital images, thus playing an important role in intelligent information processing, security and other fields.

Image recognition image recognItion Graphical stimulation acts on the sensory organs, and people recognize that it is an experienced graphic process. It is also called image re-recognition. In image recognition, there should be not only information that enters the senses at that time, but also information stored in memory.

Image recognition is a computer vision technology that can recognize objects in images and divide them into different categories. It uses image processing technologies, such as convolutional neural networks (CNN) and deep learning, to scan images, identify pixels, and classify them.

Image recognition refers to the technology of using computers to process, analyze and understand images to identify targets and objects of various different patterns. In general industrial use, industrial cameras are used to take pictures, and then the software is used for further identification according to the grayscale difference of the picture.

The meaning of image recognition is to realize the processing, analysis and understanding of images to identify various targets and objects. Face recognition technology can be used in scenarios such as security check, identity verification and mobile payment. Face recognition can improve security and facilitate users' authentication and payment.

What are the typical applications of image recognition in intelligent networked cars?

It can realize the recognition and two-way communication of moving targets under high-speed motion in a specific area, such as V2V and V2I two-way communication, real-time transmission of images, voice and data information, etc.

Pedestrian and bicycle detection: Lidar can identify pedestriansAnd non-motorized vehicles such as bicycles can provide accurate perception data even in complex traffic environments. This is very important for intelligent traffic management and accident prevention in urban traffic scenarios.

Introduction to intelligent networked vehicles. At present, the mainstream sensor products applied to environmental perception mainly include four categories: lidar, millimeter-wave radar, ultrasonic radar and camera.

What are the roles of high-precision maps in the application of intelligent networked vehicles? The introduction is as follows: (1) Map matching depends more on its a priori information. ( 2) Auxiliary environmental perception provides effective auxiliary identification for the on-board environmental perception system.

As more and more high-definition video applications enter cars, such as ADAS, 360-degree panoramic parking systems and Blu-ray DVD playback systems, their transmission rates and bandwidth can no longer meet the needs.

Intelligent networked car driving path recognition objects include vehicles, pedestrians, traffic signs, traffic lights and lane markings. According to the relevant information of the query, the main perception objects of intelligent networked vehicles are vehicles, pedestrians, traffic signs, traffic lights and lane markings, among which vehicles and pedestrians are both in a moving state and a stationary state.

What deployment modes are generally included in intelligent image recognition in the video surveillance system, its advantages and disadvantages and suitability...

There are generally two deployment modes, one is "front-end intelligent analysis" and the other is "back-end intelligent analysis". Front-end intelligent analysis is inIntelligent analysis (equivalent to edge computing) is carried out inside the camera and the analysis results are pushed to the back-end. The advantages are low cost and convenient for large-scale deployment.

This kind of low-quality image/video is directly applied to face recognition comparison, and the recognition rate is very low.

II) Prominent intuitiveness. The basis used by face recognition technology is human facial images, and human faces are undoubtedly the most intuitive source of information that can be distinguished by the naked eye. "Degmenting people by appearance" is in line with human cognitive laws. At the same time, it is convenient for manual confirmation in the later stage, and has obvious advantages such as reuse. ( III) The identification speed is fast and not easy to be detected.

2 The method of the surface pattern template method is to store several standard surface image templates or facial image organ templates in the library. When comparing, all the sampled surface image elements are matched with all the templates in the library by normalized related measures.In addition, there is also a method of combining pattern-recognition self-related networks or features with templates.

Networked storage playback Networked video storage and retrieval playback are important features of network video surveillance systems.

Data shows that in recent years, the annual compound growth rate of the total demand of the domestic video surveillance market has reached more than 20%.

specific application of image recognition

Food quality detection: The quality and composition of food can be detected and analyzed through image recognition technology, such as detecting the maturity of fruits, the fat content of meat, the freshness of vegetables, etc. .

Face recognition is widely used in automatic access control systems, identification of identity documents, banks, ATMs, home security and other fields.

Face recognition can be applied in the fields of finance, justice, military, public security, border inspection, government, aerospace, electricity, factories, education, medical care and many enterprises and institutions. With the further maturity of technology and the improvement of social identity, face recognition technology will be applied in more fields. Enterprise and residential safety and management.