What is machine vision inspection and machine vision positioning?

Machine vision detection refers to the process of identifying and locating targets with specific characteristics in images or videos, usually involving target recognition, target tracking, target segmentation and other tasks. It is one of the most core and fundamental technologies in the field of machine vision and has a wide range of application prospects.

Machine vision inspection technology mainly includes the following aspects:

1. Feature extraction: Various feature extraction algorithms can extract key features of the target from images or videos, such as color, shape, texture, etc.

2. Feature matching: The extracted target features are compared with known target features to find the position of a specific target in the image or video.

3. Target detection: By analyzing the distribution, shape, color and other features of pixels in the image, determine whether there is a target in the image, as well as the type and location of the target.

The application of machine vision inspection is very wide, including but not limited to the following aspects:

1. Defect detection: such as in the manufacturing industry to check whether there are defects in the product.

2. Security monitoring: such as target identification and tracking in video surveillance systems, automatic alarm systems, etc.

3. Autonomous driving: such as pedestrian recognition and vehicle detection in driverless cars.

4. Medical diagnosis: such as automatic diagnosis for X-ray, MRI and other medical images, detection of human organs, cells, etc.

Machine vision inspection technology is increasingly applied in various fields, not only improving the efficiency and accuracy of work, but also promoting innovation and development in various industries.

Machine vision positioning refers to the process of determining the position and attitude of the camera relative to the scene through feature extraction and matching of known objects or scenes. It is one of the important technologies in the field of machine vision, involving camera parameter calibration, feature extraction and matching, pose estimation and so on. Machine vision localization is mainly to solve the problem of reconstruction and accurate positioning of objects in three-dimensional space.

Machine vision positioning technology mainly includes the following aspects:

1. Camera calibration: Determine the internal and external parameters of the camera and determine the mapping relationship between the image coordinate system and the actual coordinate system.

2. Feature extraction and matching: extract object features in the environment, and conduct matching search in the image to find the location of a specific object.

3. Pose estimation: Using the parameters and feature matching results of the camera to estimate the translation and rotation parameters of the object relative to the camera.

The application of machine vision positioning technology mainly includes the following aspects:

1. Driverless: Through the positioning of roads and road signs, the vehicle can drive automatically.

2. Industrial automation: positioning and identification of different objects, so as to achieve intelligent production.

3. Robot navigation: Using machine vision positioning technology to achieve autonomous navigation of robots in complex environments.

4. Aerial mapping: Through the way of aerial photography, the use of machine vision positioning technology to quickly map the terrain of a large area.

Machine vision positioning technology has a wide range of applications in various fields, providing effective support for the realization of automation, intelligence and precision.

Although machine vision detection and machine vision positioning are both important technologies in the field of machine vision, their application scenarios and task objectives are different.

Machine vision detection refers to the process of finding specific objects in images or videos, often involving target recognition, target tracking, target segmentation and other tasks. Its core task is to identify and distinguish different targets, which can be applied to defect detection on industrial production lines, car driving assistance, security monitoring and other aspects.

Machine vision positioning refers to determining the position and attitude of the camera relative to the scene through feature extraction and matching of known objects or scenes in a specific scene. Its core task is to measure or recover the three-dimensional space position and attitude of the object or scene. The application scenarios of machine vision positioning are more extensive, such as: robot navigation, unmanned vehicles, aerial mapping, virtual reality and so on.

The difference between the two lies in the difference of the target. Detection is to search the target in the scene at the location of the uncertain target. Positioning is knowing where the target is and measuring the target's position and attitude in the scene.