增强VGG16模型的多视图和空间脱落,用于蚊子载体的分类
Wanchalerm Pora1, Natthakorn Kasamsumran1, Katanyu Tharawatcharasart1
1Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
PloS one
|July 24, 2023
概括
这项研究使用修改的VGG16卷积神经网络 (CNN) 模型增强了蚊子分类,达到99%以上的准确性. 改进后的模型具有强大性和适应性,可用于有效的昆虫学监测,以控制蚊子传播的疾病.
科学领域:
- 计算机科学 计算机科学
- 昆虫学 昆虫学是一门学科.
- 公共卫生 公共卫生
背景情况:
- 蚊子会传播重要传染病,如疟疾和登革热.
- 现有的VGG16模型对蚊子分类有希望,但需要改进.
- 准确的蚊子识别对于疾病控制至关重要.
研究的目的:
- 提高蚊子分类VGG16网络的准确性和稳定性.
- 通过整合空间丢失和多视图输入来开发一个新的分类器.
- 评估模型在各种蚊子数据集上的表现.
主要方法:
- 实施了基于VGG16的四个模型:早期组合,中期组合,晚期组合和整体组合.
- 集成的空间脱落层用于规范化和多视图输入.
- 开发了一个结合四种模型的分类器,并在韩国和泰国数据集上进行评估.
主要成果:
- 在参考数据集上,平均准确度从83.26%提高到99.77%.
- 在具有低标准偏差 (<2%) 的新泰国数据集上实现了超过99%的准确性.
- 在对各种数据集进行重新训练后,通过恢复高准确度来证明强度.
结论:
- 增强的VGG16分类器实现了高精度和低变化,独立于特定的数据集.
- 该模型对数据集的变化具有弹性,并且可以通过再培训来适应.
- 分类器和数据集可以支持对蚊子传播疾病的预防进行快速昆虫学监测.
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