Computer Vision in Smart Manufacturing: Defect Detection and Predictive Maintenance

May 27, 2026

Story

In 1943, at the University of Chicago, Warren McCulloch and Walter Pitts published A Logical Calculus of the Ideas Imminent in Nervous Activity, leading to the foundational work on neural network concepts and modern-day artificial intelligence (AI). Now that AI is moving more to the edge, computer vision is becoming a critical tool. Computer vision systems use machine learning and deep learning to visualize and analyze data for defect detection and predictive maintenance in smart manufacturing

Defect Detection

Using computer vision in manufacturing makes manufacturers more consistent, efficient, and accurate in detecting defects within goods. Whether computer vision is used during the full manufacturing process, with multiple cameras positioned on its way to completion, or as a final product review, cameras can identify missing parts, assembly flaws, and cosmetic imperfections, lessening the degree of product recalls.

Predictive Maintenance

Like defect detection, predictive maintenance analyzes data from cameras and sensors in real-time. However, predictive maintenance is not about the product, it helps to keep manufacturing robots and machines online and readily available. When combined with machine learning, these systems can alert staff to anomalies in machine movement, vibration, temperature, wear, and much more, reducing downtime and production costs.

Computer vision systems employ data to:

• Classify Images – an image is assessed while the system looks for any irregularities between what it has learned is correct and what it is currently visualizing 
• Detect Objects – shows where the defects are and highlights the problem areas
• Segment Images – the areas showing abnormalities are isolated down to pixels 

Training the Model

A major challenge with computer vision is training the learning model. Models need to be trained to identify specific defects, products/parts, and the physical environment, such as light. A change in lighting, including brightness, shadows, and glares, can cause problems when the system is comparing images it has learned to images now being evaluated.

AI is insistent on moving farther to the edge, and growth in computer vision will be a necessary component for this trajectory. As the technology grows, so will the number of industries embracing computer vision to lower costs and improve product performance.