Advantages of machine vision applications

Among these methods, structured light technology is the most mature and has been widely used in industrial 3D machine vision. In the structured light method, a series of graphics are projected onto an object, and then the camera or sensor detects the distortion and deformation of these graphics. Next, image processing and triangulation algorithms convert these distortions and deformations into 3D point cloud data, which can be directly used for analysis or exported to a variety of CAD modeling formats. For example, the Texas Instruments (TI) DLP system can generate non-contact, high-precision 3D data in real time, making 3D machine vision easier to implement. In this solution, the DLP chipset can provide different DMD (digital micromirror device) sizes, pixel pitches, resolutions, and wavelength ranges.

As technology develops, reconstructing 3D information using time-of-flight technology has become the most important method for machine vision. Time-of-flight technology uses a laser scanner to estimate the distance between the light source and the target based on the time it takes for the light to reach the target and return. Analog Devices (ADI)'s ADSD3100 is a CMOS 3D time-of-flight-based 3D depth and 2D visible light imager that can be integrated into a 3D sensor system.

The required functional blocks include analog-to-digital converters (ADCs), pixel bias circuits, and sensor control logic, which are built into the chip to enable simple, cost-effective solutions in the system. The ADSD3100 electrically interfaces with the host system through the MIPI, Camera Serial Interface 2 (CSI-2) interface.

Conclusion

Like other capabilities such as automation, machine learning, deep learning, and neural networks, machine vision is a capability, not an industry. It is a capability that can be integrated into other technologies and processes to benefit industries and improve business efficiency. Edge intelligence or edge AI is the next big trend in the development of machine vision technology, which moves machine learning from the cloud to the edge of physical devices. All of this is closely related to the future of machine vision, and advances in edge computing make it possible to apply deep learning capabilities in traditional machine vision tasks.

It can be seen from the forecasts of research institutions that the machine vision market is growing rapidly, and companies are increasingly looking to automate business processes by introducing automation functions such as machine vision and robots. This trend will not slow down soon.