Won-Seok Choi  Byoung-Tak Zhang

10.5626/JOK.2026.53.1.74

hierarchical object detection unsupervised instance segmentation semantic duplication removal Environment Recognition

This study proposes a hierarchical object detection method (HOD-SAM) designed to effectively eliminate geometric and semantic duplications among masks produced by the Segment Anything Model (SAM), while organizing them into a hierarchical structure. The method constructs a tree structure based on the inclusion relationships between object masks and utilizes self-supervised feature extractors to remove semantically duplicated masks. This approach not only addresses duplication issues in SAM-based instance detection but also enhances downstream task performance. We validate the effectiveness of HOD-SAM in multiple tasks such as object instance segmentation and place classification within the context of place recognition. Experiments conducted on the COCO and Places365 datasets reveal that the proposed hierarchical recognition structure outperforms the original SAM model in terms of both accuracy and efficiency, indicating its potential as a general-purpose perception module for broader applications in understanding complex environments.