US 12,488,570 B2
Object detection method for detecting one or more objects using a plurality of deep convolution neural network layers and object detection apparatus using the same method and non-transitory storage medium thereof
Peter Chondro, Surabaya (ID)
Assigned to Industrial Technology Research Institute, Hsinchu (TW)
Filed by Industrial Technology Research Institute, Hsinchu (TW)
Filed on Nov. 24, 2022, as Appl. No. 17/993,881.
Prior Publication US 2024/0177456 A1, May 30, 2024
Int. Cl. G06V 20/58 (2022.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01)] 19 Claims
OG exemplary drawing
 
1. An object detection method for detecting one or more objects using a plurality of deep convolution neural layers, the method comprising:
obtaining a set of a plurality of object annotated images which are in a source domain and have a first image style;
obtaining a minority set of a plurality of object annotated images which are in a target domain and having a second image style;
obtaining a majority set of a plurality of unannotated images which are in the target domain and having the second image style;
performing an image style transfer from the source domain to the target domain by converting the plurality of object annotated images in the source domain from having the first image style into having the second image style to generate a converted set of object annotated images having the second image style;
training a pseudo-labeler with supervised training based on the converted set of object annotated images having the second image style and the minority set of the object annotated images in the second image style;
generating, by the pseudo-labeler, object annotations for the majority set of the plurality of unannotated images in the second image style to change from the majority set of a plurality of unannotated images into a majority set of a plurality of annotated images; and
performing an active domain adaptation by adapting the minority set of a plurality of object annotated images, the converted set of object annotated images, and the majority set of the plurality of annotated images so as to generate an object detection model.