Within the machine vision space, an ebb and flow of buzzwords emerge at tradeshows or in the press every few years. While certain technologies have been perhaps overhyped or released without any real-world applications in mind, many others are successfully deployed on factory floors and in warehouses today.
For example, technologies such as multispectral imaging, collaborative robots (cobots), and — yes — artificial intelligence (AI) and deep learning, can boost efficiency, enhance productivity and output, improve quality, and help drive revenue. System success is contingent upon not only identifying the right application, but also understanding how the requisite components of a machine vision system work together. Lighting, for example, represents a crucial consideration when designing and specifying a system, as it is fundamental to image quality. In addition, lighting implementations vary greatly from application to application — more so than other system components — so understanding how lighting impacts the rest of the system lays the foundation for system success.