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Open Access

Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection

College of Computer Science and Technology, Qingdao University, Qingdao 266070, China
Department of Software Engineering and Game Development, Kennesaw State University, Atlanta, CA 30060, USA
Technology Center of Qingdao Customs District, Qingdao 266070, China
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Abstract

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.

Keywords

bacterial detectionlarge modelfeature fusionglobal information

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Big Data Mining and Analytics
Volume 8 Issue 1,
February 2025
Pages 1-17
DOI: 10.26599/BDMA.2024.9020078
Cite this article:
Liu Z, Huang Y, Wang J, et al. Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection. Big Data Mining and Analytics, 2025, 8(1): 1-17. https://doi.org/10.26599/BDMA.2024.9020078

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Received: 07 March 2024
Revised: 06 October 2024
Accepted: 16 October 2024
Published: 19 December 2024
© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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