Learning method and learning device for fluctuation-robust object detector based on CNN using target object estimating network adaptable to customers' requirements such as key performance index, and testing device using the same
Abstract:
A method for learning parameters of an object detector by using a target object estimating network adaptable to customers' requirements such as KPI is provided. When a focal length or a resolution changes depending on the KPI, scales of objects also change. In this method for customer optimizable design, unsecure objects such as falling or fallen objects may be detected more accurately, and also fluctuations of the objects may be detected. Therefore, the method can be usefully performed for military purpose or for detection of the objects at distance. The method includes steps of: a learning device instructing an RPN to generate k-th object proposals on k-th manipulated images which correspond to (k−1)-th target region on an image; instructing an FC layer to generate object detection information corresponding to k-th objects; and instructing an FC loss layer to generate FC losses, by increasing k from 1 to n.
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