Camera Module Inspection
Limitations of Current Inspection Processes
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
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.