When best practices are followed, machine vision and deep learning-based imaging systems are capable of effective visual inspection and will improve efficiency, increase throughput, and drive revenue.

For decades, machine vision technology has performed automated inspection tasks—including defect detection, flaw analysis, assembly verification, sorting, and counting—in industrial settings. Recent computer vision software advances and processing techniques have further enhanced the capabilities of these imaging systems in new and expanding uses. The imaging system itself remains a critically important vision component, yet its role and execution can be underestimated or misunderstood.

Without a well-designed and properly installed imaging system, software will struggle to reliably detect defects. For example, even though the imaging setup in Figure 1 (left) displays an attractive image of a gear, only the image on the right clearly shows a dent. When best practices are followed, machine vision and deep learning-based imaging systems are capable of effective visual inspection and will improve efficiency, increase throughput, and drive revenue. This article takes an in-depth dive into the best practices for iterative design and provides a roadmap for success for designing each type of system. Read More