As a machine vision engineer with many years of experience, I will go into detail about the different characteristics of 2D and 3D vision technologies, their application scenarios, and the problems they can solve. In this field, 2D and 3D vision technology are the key technologies to achieve automation and intelligent manufacturing, and they have a wide range of applications in many fields such as industrial inspection, robot navigation, and quality control.
2D vision technology
2D vision technology mainly processes flat images, and accomplishes various tasks through the analysis and understanding of two-dimensional images. These technologies are typically based on the following aspects:
Image Acquisition: Capture images using industrial cameras, which may be grayscale or color, with different types of light sources and illumination techniques to enhance image quality.
Pre-processing: Includes filtering and denoising, enhanced contrast, edge detection, and more to improve the recognizability of features in the image.
Feature extraction: Algorithms identify key features in images, such as straight lines, corners, contours, textures, and more.
Pattern Recognition: Classify and recognize extracted features using template matching, machine Xi learning, or deep Xi methods.
Measurement & Positioning: Dimensioning, positioning, and more of objects in an image are performed 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 Scenarios:
2D vision technology is widely used in the manufacturing industry, including product assembly, label inspection, print quality inspection, part size measurement, and more. 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, which involves capturing the three-dimensional shape and spatial position of an object, provides much richer information. These techniques 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: A laser sensor measures the surface profile of an object by emitting a laser beam and capturing its reflected light.
Structured Light: Projectes a specific light pattern onto the surface of an object to calculate the three-dimensional shape of the object based on the deformation of the texture.
Time Flight (ToF): Determines the distance of an object by measuring the time it takes for a light wave to return from the time it is transmitted.
3D Reconstruction: Reconstruct a 3D model from a series of 2D images using techniques such as multi-view geometry and point cloud processing.
Applicable Scenarios:
3D vision technology is used in applications that require depth information, such as robot gripping, 3D modeling of objects, assembly inspection of complex components, and environmental perception and navigation.
Comparison of 2D vs. 3D vision technology
Advantages and Limitations:
2D vision technology is more cost-advantageous, and its systems are generally simpler and cheaper, while 3D vision technology has the advantage of providing more comprehensive spatial data. However, 3D technology is often higher than 2D technology in terms of processing speed, system complexity, and cost.
Problem solved:
2D vision technology is good at dealing with 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.
Application examples
In the automotive industry, 2D vision systems can be used to inspect the quality of the paint on car bodies, identify and inspect the marking on parts. 3D vision systems can be used to precisely position and guide robots for welding, assembling 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). 3D vision systems can be used to inspect the height and volume of components on a circuit board to ensure proper installation.
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 parcels, optimize storage space and transport efficiency.
conclusion
Both 2D and 3D vision technologies have their own characteristics and advantages, and choosing the right technology depends on the specific application needs, cost budget, and system complexity. As technology evolves, so do the two technologies, such as augmented reality (AR) technology, which combines 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 Xi technology, we can foresee that 2D and 3D vision technology will have more extensive and in-depth applications.