التفاصيل البيبلوغرافية
| العنوان: |
Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection |
| المؤلفون: |
Zongduo Liu, Yan Huang, Jian Wang, Genji Yuan, Junjie Pang |
| المصدر: |
Big Data Mining and Analytics, Vol 8, Iss 1, Pp 1-17 (2025) |
| بيانات النشر: |
Tsinghua University Press, 2025. |
| سنة النشر: |
2025 |
| المجموعة: |
LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: |
bacterial detection, large model, feature fusion, global information, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: |
Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2096-0654 |
| Relation: |
https://www.sciopen.com/article/10.26599/BDMA.2024.9020078; https://doaj.org/toc/2096-0654 |
| DOI: |
10.26599/BDMA.2024.9020078 |
| URL الوصول: |
https://doaj.org/article/10627bbf674c4e9ea220bc776fe265e2 |
| رقم الانضمام: |
edsdoj.10627bbf674c4e9ea220bc776fe265e2 |
| قاعدة البيانات: |
Directory of Open Access Journals |