Inline Inspection

Industrial quality inspection is particularly suitable for AI due to its repetitive nature and high level of predictability. This makes it possible to collect a dataset of images that can be used to train a custom neural network for classification, object, or anomaly detection.

While products that are nearly identical and need to be measured for tolerance or conformity are probably easier to solve using a stack of traditional algorithms, products that require a subjective evaluation, sometimes from an experienced visual inspector, are highly suitable for deep learning-based inspection systems.

For example, medical device inspection benefits significantly from using AI inspection to detect a variety of defects including zipper lines, dirt, and gels across a variety of medical balloon types. Addressing this type of inspection with a traditional rule-based system, including solving the edge (or corner) cases that come along with multiple defect categories and inspection targets, would make this solution incredibly complex and time-consuming to implement.