Construction of large-scale Security image resource library based on active learning
This paper proposes a framework for constructing and optimizing an X-ray security inspection image resource library based on active learning.The framework uses the principles and methods of active learning to select the most valuable data from a large number of unmarked security inspection image data for annotation and learning.At the same time,the labeled data is used to train and optimize various models in data screening and annotation.The framework can continuously adjust the data selecting standards according to the data in the resource library.This framework can quickly establish a training resource library at the supervision site,continuously optimize the on-site model according to local data,improve the efficiency and accuracy of security inspection work,and reduce labor costs and error rates.The framework has good generalization and can be extended to different application scenarios such as subways,customs,postal services and aviation civil aviation,providing more reliable technical support for regulatory departments.
active learningautomatic annotationX-ray security inspection
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