Machine vision in automation isn't inherently difficult from the start; it follows a clear application hierarchy. Based on real-world project experience, automated vision can be broadly categorized into three difficulty levels.
First: Basic visual inspection, which addresses whether an object exists, if its position is correct, and if its orientation is proper. Commonly used in sorting, loading/unloading, and simple assembly equipment, this approach primarily relies on value-based segmentation and template matching.
Secondly: Stabilize visual inspection for dimensional measurement and detection of microscopic appearance defects over long periods, commonly used in continuous production lines such as lithium batteries and photovoltaics. This relies heavily on imaging solutions, lighting control, and system stability.
Third: High-difficulty industrial vision. This addresses small targets, complex backgrounds, and highly reflective materials—common in semiconductor and advanced manufacturing. It requires custom optical imaging combined with specialized algorithms.
The true technical gap in machine vision often lies in the stability and replicability of Category 2 and Category 3 applications.
