On Improving Performance of Surface Inspection Systems by On-line Active Learning and Flexible Classifier Updates
Sprache des Titels:
Classification of detected events is a central
component in state-of-the-art surface inspection systems
that still relies on manual parametrization. While
machine-learned classifiers promise supreme accuracy,
their reliability depends on complete and correct annotation
of an extensive training database, leaving the risk
of unpredictable behavior in changing production environments.
We propose an active learning-based training
framework, which selectively presents questionable
events for user annotation and is capable of online operation.
Evaluation results on two data streams from
microfluidic chips and elevator sheaves production show
that annotation effort can be reduced by 90% with negligible
loss of accuracy. Simulation runs introducing new
event classes show that the on-line active learning procedure
is both efficient in terms of learning speed and
robust in maintaining the accuracy levels of existing
classes. The results underline the feasibility and potential
of our approach that significantly reduces the required
effort for inspection system setup and adapts to
changes in the production process.