التفاصيل البيبلوغرافية
| العنوان: |
Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5 |
| المؤلفون: |
Yin Cai, Pengxin Tian, Haoran Song, Yuzhen Yin, Guannan Si, Ruifeng Liu |
| المصدر: |
Electronics; Volume 12; Issue 14; Pages: 3121 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
seismic station, deep learning, kalman filtering, centroid matching, multi-object tracking, YOLOv5, time-series data |
| الوصف: |
In recent years, human interference in seismic-station environments has posed challenges to the quality and accuracy of seismic signals, making data processing difficult. To accurately identify interference caused by personnel and ensure the reliability of seismic-network instrument detection data, it is necessary to track the detected targets across consecutive frames. Deep neural networks have made significant progress in this field. Therefore, an intelligent identification solution for environmental interference at seismic stations is proposed, which combines deep learning with multi-object tracking techniques. A centroid-matching tracking algorithm based on Kalman filtering is introduced to identify the entry/exit timestamps, alongside motion trajectories of interfering individuals, thereby marking the anomalous data caused by the presence of interfering personnel in seismic time-series data. Experimental results demonstrate that this research provides an effective solution for intelligent identification of environmental interference in seismic station environments. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
https://dx.doi.org/10.3390/electronics12143121 |
| DOI: |
10.3390/electronics12143121 |
| الاتاحة: |
https://doi.org/10.3390/electronics12143121 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.99FEBA61 |
| قاعدة البيانات: |
BASE |