| User | ISRI_SKKU |
|---|---|
| Publication | |
| Implementation | |
| Views | Single |
| Test image modalities | RGB-D |
| Description | Submitted to: BOP Challenge 2025 Training data: provided PBR + custom synthetic Notes: Our cascaded YOLO–YOLACT framework integrates additional networks to maximize recall and improve precision. A Box Reorganization algorithm redefines precise RoIs, followed by an Image Classifier and Feature-Level Refinement to enhance class accuracy. Scene-Level Fusion combines class, score, and box information, yielding robust segmentation of small and heavily occluded objects. |
| Computer specifications | CPU: Intel(R) Core(TM) i7-10700 CPU @ 2.90GHz; RAM: 64GB; GPU: NVIDIA TITAN X (Pascal) |
| Date | Submission name | Dataset | ||
|---|---|---|---|---|
| 2025-10-01 15:27 | ORSP-NET_Segm | LM-O |