Manual Visual Inspection: Visual inspection has undergone significant transformation over the past decades. Initially, it was performed entirely manually – by trained specialists whose experience and attention were critical for detecting defects. However, human limitations such as fatigue, subjective judgment, and limited repeatability made manual inspection error-prone, costly, and difficult to scale.

Rule-Based Systems: With the rise of digital technologies, rule-based camera systems were introduced. These traditional automated solutions enabled faster and more consistent inspections. Based on predefined rules and classical image processing, they perform reliably when inspecting clearly defined defect patterns. Their main weakness, however, is a lack of flexibility. As soon as product variations, complex surface textures, or irregular defects are involved, these systems reach their limits and require frequent manual adjustments and support.

AI-Based Visual Inspection: AI-based systems represent the latest advancement in visual inspection. Using deep learning, they mimic human cognitive abilities. Rather than relying on fixed rules, they learn what constitutes a “good” or “bad” part by analyzing real production data. These systems can detect previously unknown defects and learn highly complex patterns, achieving inspection accuracy comparable to that of a consistently attentive human.