Qianqian Zhen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Qianqian Zhen has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Guoying Liu
School of Software Engineering, Anyang Normal University, Anyang 455000, China
1 shared publication - Liang Wu
School of Software Engineering, Anyang Normal University, Anyang 455000, China
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 24 April 2024
- Published by MDPI AG in Algorithms
- Vol. 17 (5) , 174
An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8
Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The current research aimed to determine the precise location of OBIs with rubbing images. Given the low clarity, severe noise, and cracks in oracle bone inscriptions, the mainstream networks within the realm of deep learning possess low detection accuracy on the OBI detection dataset. To address this issue, this study analyzed the significant research progress in oracle bone script detection both domestically and internationally. Then, based on the YOLOv8 algorithm, according to the characteristics of OBI rubbing images, the algorithm was improved accordingly. The proposed algorithm added a small target detection head, modified the loss function, and embedded a CBAM. The results show that the improved model achieves an F-measure of 84.3%, surpassing the baseline model by approximately 1.8%.
- 24 April 2024
- Published by MDPI AG in Algorithms
- Vol. 17 (5) , 174
Top Cited PapersOpen AccessAn Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8
Ancient Chinese characters known as oracle bone inscriptions (OBIs) were inscribed on turtle shells and animal bones, and they boast a rich history dating back over 3600 years. The detection of OBIs is one of the most basic tasks in OBI research. The current research aimed to determine the precise location of OBIs with rubbing images. Given the low clarity, severe noise, and cracks in oracle bone inscriptions, the mainstream networks within the realm of deep learning possess low detection accuracy on the OBI detection dataset. To address this issue, this study analyzed the significant research progress in oracle bone script detection both domestically and internationally. Then, based on the YOLOv8 algorithm, according to the characteristics of OBI rubbing images, the algorithm was improved accordingly. The proposed algorithm added a small target detection head, modified the loss function, and embedded a CBAM. The results show that the improved model achieves an F-measure of 84.3%, surpassing the baseline model by approximately 1.8%.