In recent years, there have been many new technologies that have driven the development of the machine vision industry, especially in terms of recognition capabilities, which have become the core competitiveness of machine vision. The visual recognition function can check for the presence or absence of an item and determine if there is an assembly defect. The visual recognition may also be used to locate an object or the like, for example, for a robot to position a target object, or to automatically classify an object.
The emergence of 3D machine vision systems has brought surprises to recognition. In most cases, the 3D vision system is able to detect object objects in more detail. Whether for more advanced identification in inspection applications or better object differentiation in metrology applications, 3D vision systems bring more advanced features.
In addition, in hyperspectral imaging and color imaging, hyperspectral technology will allow machine vision to detect spectra beyond the visible light for more powerful image quality, while color imaging allows for advanced color analysis in inspection applications.
In addition, the development of deep learning plays an important role in promoting machine vision recognition. By continuously learning complex object detection and classification techniques, machine vision systems can collect more knowledge and experience from the surrounding environment, and finally achieve autonomous and accurate identification of objects. . Machine vision recognition is the core process in machine vision applications, driving machine vision to a brighter future.