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Camera Module Inspection


Limitations of Current Inspection Processes

Low Accuracy

There is such a slight difference between the defective area

and the normal area that distinction between the two is

difficult to the naked eye. Therefore, the level of accuracy

of the inspection provided by machine vision algorithms

alone is relatively low.

High Optimization Costs

Due to the short model replacement cycle and the varying

characteristics of different models, it is costly to optimize

the machine vision algorithm.

Why Camera Module Inspection is Difficult

Difficult to Distinguish Between Defective

and Normal Areas

In the case of certain defects, such as glue overflow or

black spots, the shape and characteristics of defects

are rather similar to those of normal areas, making it a

challenge to accurately pick out the defects. As such,

with the current machine vision algorithms, continuous

optimization is required for the detection quality to reach

a sufficient level.



Benefits Offered by SuaKIT

Improved Inspection Accuracy

Deep learning algorithms enable achievement of high

accuracy that goes beyond the detection quality of

machine vision.

Reduced Optimization Costs

The only required initial learning involves inputting of data

of labeled images. Following that, the solution is able to

automatically perform defect detection, leading to

minimized optimization costs.

Increased Efficiency of Human Capital Management

As the inspection accuracy improves, automatic inspection

becomes possible, and various machines can be operated

by one employee.