Multi-level transferable region-based domain adaptive object detection apparatus and method

A multi-level transferable region-based domain adaptive object detection apparatus comprising:

• an image-level feature part configured to extract an image-level feature by providing unlabeled target data to a plurality of convolutional layers;
• an RPN-level feature part configured to determine region candidates by providing the image-level feature to a region proposal network (RPN) and performing RPN-level domain adaptation and transferable region filtering;
• and
• an instance-level feature part configured to extract a region of interest (RoI) pooling feature by conducting dynamic instance sampling on the region candidates,
• wherein the dynamic instance sampling includes dynamically determining a number of the region candidates in a target domain by multiplying a divergence between objectness scores of top N regions of the region candidates from a source domain and objectness scores of top N regions of the region candidates from the target domain by N, and
• wherein the image-level feature part, the RPN-level feature part, and the instance-level feature part are each implemented via at least one processor.