An Oracle Bone Inscription Detector Based on Multi-Scale Gaussian Kernels  

作　　者：Guoying Liu Shuanghao Chen Jing Xiong Qingju Jiao Guoying Liu;Shuanghao Chen;Jing Xiong;Qingju Jiao(School of Computer and Information Engineering, Anyang Normal University, Anyang, China;School of Computer and Engineering, Zhengzhou University, Zhengzhou, China)

机构地区：[1]School of Computer and Information Engineering, Anyang Normal University, Anyang, China [2]School of Computer and Engineering, Zhengzhou University, Zhengzhou, China

出　　处：《Applied Mathematics》2021年第3期224-239,共16页应用数学（英文）

摘　　要：The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn.The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn.

关 键 词：Oracle Bone Inscriptions Deep Learning Object Detection Hourglass Network 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]