Machine vision 2D technology has been developed for more than 30 years. It is relatively mature and widely used in the field of product quality control automation, but it is difficult to meet the current technical needs.
Nowadays, product quality in the market requires higher and higher precision. Compared with 2D, machine vision 3D technology will be more popular with manufacturers. 3D vision can measure the shape information that can not be generated by 2D system. Therefore, shape-related features such as flatness, surface angle and volume can be measured.
All components of the 3D sensor are firmly mounted on a single optical-mechanical component to ensure repeatability, focal length is locked in place relative to the plane of the transmitter and the imager, and temperature compensation is included to correct the movement caused by metal creep.
Another advantage of 3D machine vision is that, for example, large objects such as truck frames can be scanned with multiple scanners.
With more applications of artificial intelligence coming to the ground, in-depth learning has become a popular trend in machine vision detection. In-depth learning is an area of machine learning, which enables computers to train and learn through convolutional neural networks and other architectures. It mimics the way the human brain works by processing data and creating patterns for decision-making.
In the next few years, in-depth learning technology will continue to play an important role.