TY - JOUR
KW - Data Augmentation
KW - CycleGAN
KW - Non-Maximum Suppression
KW - Object Detection
KW - Oracle Bone Script
AU - Xiuan Wan
AU - Yuchun Fang
AU - Jiahua Wu
AU - Shouyong Pan
AB - Character detection is essential for subsequent Oracle Bone Inscription (OBI) research. However, the lack of labeled data and the complexity of small and dense OBI characters are the main difficulties in OBI detection
research. In this paper, we propose a framework for rubbing generation that can automatically build up large scale rubbing samples with verisimilar scenarios to noisy wild OBI through geometric and morphological
construction combined with style transferring. Moreover, we propose a semantic-enhanced detection model aiming at small and dense OBI through the fusion of multi-resolution feature maps with the enriched feature in the YOLOv5s backbone. We introduce the higher resolution and the Soft-NMS into the proposed OBI detection model to solve the overlapping of small and dense OBI characters. The augmented dataset improves the performance of benchmark object detection models in the real OBI detection task when sufficient data is lacking. Furthermore, the proposed OBI detection model can provide easy and preferable access to OBI detection even with a small number of labeled data and obtain preferable results. Experiments ascertain the effectiveness of the proposed OBI generation framework and the proposed OBI detection model.
IS - In press
M1 - In press
N2 - Character detection is essential for subsequent Oracle Bone Inscription (OBI) research. However, the lack of labeled data and the complexity of small and dense OBI characters are the main difficulties in OBI detection
research. In this paper, we propose a framework for rubbing generation that can automatically build up large scale rubbing samples with verisimilar scenarios to noisy wild OBI through geometric and morphological
construction combined with style transferring. Moreover, we propose a semantic-enhanced detection model aiming at small and dense OBI through the fusion of multi-resolution feature maps with the enriched feature in the YOLOv5s backbone. We introduce the higher resolution and the Soft-NMS into the proposed OBI detection model to solve the overlapping of small and dense OBI characters. The augmented dataset improves the performance of benchmark object detection models in the real OBI detection task when sufficient data is lacking. Furthermore, the proposed OBI detection model can provide easy and preferable access to OBI detection even with a small number of labeled data and obtain preferable results. Experiments ascertain the effectiveness of the proposed OBI generation framework and the proposed OBI detection model.
PY - 9998
SE - 1
SP - 1
EP - 14
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character
UR - https://www.ijimai.org/journal/bibcite/reference/3604
VL - In press
SN - 1989-1660
ER -