As a machine vision engineer with many years of experience, I will introduce in detail the different characteristics, application scenarios and problems they can solve of 2D and 3D vision technologies. In this field, 2D and 3D vision technologies are key technologies for realizing automation and intelligent manufacturing. They are widely used in many fields such as industrial inspection, robot navigation, and quality control.
2D vision technology
2D vision technology mainly processes flat images and completes various tasks through the analysis and understanding of two-dimensional images. These technologies are usually based on the following aspects:
Image capture: Use industrial cameras to capture images, which may be grayscale or color, using different types of light sources and lighting techniques to enhance image quality.
Preprocessing: including filtering and denoising, contrast enhancement, edge detection, etc. to improve the recognizability of features in the image.
Feature extraction: Identify key features in images through algorithms, such as straight lines, corners, contours, textures, etc.
Pattern recognition: Classification and recognition of extracted features using template matching, machine learning or deep learning methods.
Measurement and positioning: Measure the size, position, etc. of objects in the image to determine their accurate geometric parameters.
Defect detection: Identify defects in products by comparing images to standard templates or using algorithms to detect anomalies.
Applicable scene:
2D vision technology is widely used in manufacturing, including product assembly, label inspection, printing quality inspection, part size measurement, etc. It is very effective in situations where the surface features of the object are obvious and the depth information is not required.
3D vision technology
3D vision technology involves capturing the three-dimensional shape and spatial position of objects, which provides richer information. These technologies include:
Stereo vision: Use two or more cameras to shoot the same scene from different angles, and calculate the depth information of the object through similar triangulation.
Laser scanning: Measure the surface profile of an object by emitting a laser beam from a laser sensor and capturing its reflected light.
Structured light: Projects specific light patterns onto the surface of an object, and calculates the three-dimensional shape of the object based on the deformation of the texture.
Time of Flight (ToF): Determines the distance of an object by measuring the time it takes for a light wave to travel from emission to return.
3D reconstruction: Use multi-view geometry, point cloud processing and other technologies to reconstruct a 3D model from a series of 2D images.
Applicable scene:
3D vision technology is used in applications that require depth information, such as robot grasping, three-dimensional object modeling, assembly inspection of complex components, and environmental perception and navigation.
Comparison of 2D and 3D Vision Technologies
Advantages and limitations:
2D vision technology is more advantageous in terms of cost, and its systems are usually simpler and cheaper, while 3D vision technology is superior in providing more comprehensive spatial data. However, 3D technology is usually higher than 2D technology in terms of processing speed, system complexity and cost.
solved problem:
2D vision technology is good at handling problems that do not require depth information, such as barcode recognition, text recognition, color detection, etc. 3D vision technology can solve problems that require precise depth information, such as precise positioning of objects, detection of irregular shapes, and understanding of complex spatial relationships.
Applications
In the automotive manufacturing industry, 2D vision systems can be used to inspect the paint quality of car bodies and identify and inspect markings on parts. The 3D vision system can be used to accurately position and guide robots for welding, assembly or handling heavy parts.
In electronics manufacturing, 2D vision systems are commonly used to inspect component placement and soldering quality on printed circuit boards (PCBs). The 3D vision system can be used to detect the height and volume of components on the circuit board to ensure correct installation of components.
In logistics automation, 2D vision systems can be used to quickly scan and identify label information on packages. 3D vision technology can be used to measure the volume of packages to optimize storage space and transportation efficiency.
in conclusion
2D and 3D vision technologies each have their own characteristics and advantages. Choosing the appropriate technology needs to be determined based on specific application requirements, cost budget and system complexity. With the development of technology, the two technologies are constantly integrated, such as using augmented reality (AR) technology to combine 2D images and 3D models to provide users with a more intuitive visual experience. In the future, with the further development of artificial intelligence and machine learning technology, we can foresee that 2D and 3D vision technology will have more extensive and in-depth applications.
