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.

As manufacturers worldwide seek to enhance their machines and equipment with online connectivity—an example of the Industrial Internet of Things (IIoT)—they are realizing this technology’s remarkable potential. I

The emergence of autonomous mobile robots (AMR), which can independently transport materials around a facility using built-in Lidar scanners and 3D cameras, can make automating tasks faster, less expensive and easier to deploy overall.

In all types of industries, machine learning (ML) tools are finding the needle in the haystack of data, augmenting quality and safety professionals with a new kind of intelligence that can unlock hidden data patterns that are impossible for the human mind or eye to absorb.

Brand appearance plays a significant role in consumer choice, and the manufacturer competes against larger players with much deeper marketing budgets.

Robot sales have surged over the last few years as companies in every industry recognize robotics and automation can help them compete globally. 

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.