Deep learning is now more user-friendly and practical than ever and together with other vision technologies opens up new application areas, making the inclusion of vision inspection as part of Industry 4.0 even more beneficial.
One constant in the ever-evolving machine vision space is the need for high-quality, consistent lighting. New challenges in the design and specification of machine vision systems require an innovative approach to lighting selection.
In this article, deep learning refers to developments during the last few years that have enabled applying the technique to entire images in the industrial machine vision space.
The discipline of machine vision encompasses imaging technologies and methods to perform automatic inspection and analysis in various applications, such as verification, measurement, and process control.
Lenses play a crucial role in the quality of the images produced by a machine vision system since they determine the sharpness of the image on the camera sensor. Lenses can influence image quality in a variety of ways.
While machine vision applications have been highly successful for decades using "analytical" vision tools, deep learning is able to successfully solve very complex classification and object detection problems with ease.
Product packaging - in an extremely broad range of markets from food to pharma - frequently incorporates an extremely important sealing technology called “tamper evident” seals.
Autonomous machine vision inspection provides quick, automated defect recognition and can implement the knowledge it gains, thereby decreasing false alarms and erroneous scrap.