多角度,跨领域的融合策略增强了自动化昆虫识别和层次分类:关于杀手虫 (Hemiptera: Reduviidae) 的案例研究
Xinkai Wang1, Huaiyu Liu1, Zhuo Chen1
1State Key Laboratory of Agricultural and Forestry Biosecurity, MOA Key Lab of Pest Monitoring and Green Management, College of Plant Protection, China Agricultural University, Beijing, China.
Cladistics : the international journal of the Willi Hennig Society
|February 12, 2026
概括
自动化昆虫识别通过多角度和生态图像得到了改进,提高了生物多样性和害虫管理的准确性. 这种方法有助于分类物种和更高的分类学等级,即使是看不见的昆虫.
科学领域:
- 昆虫学 昆虫学是一门学科.
- 计算机科学 计算机科学
- 生物多样性信息学 生物多样性信息学
背景情况:
- 自动化昆虫识别对于生物多样性监测和害虫管理至关重要.
- 当前的深度学习模型由于有限的角度和简化的背景依赖,与多样化,现实世界的昆虫图像作斗争.
- 昆虫分类学专业知识不断下降,需要强大的自动识别系统.
研究的目的:
- 开发和评估一种自动化昆虫识别系统,克服当前深度学习方法的局限性.
- 通过整合各种图像类型和视角来提高昆虫识别的准确性和概括性.
- 评估模型对将昆虫分类到更高的分类学等级的能力.
主要方法:
- 创建了一个由11,915个专家验证的刺客虫 (Hemiptera: Reduviidae) 图像的数据集,包括标准,转盘捕捉和生态图像.
- 使用ConvNeXt-B深度学习架构进行图像分类和层次分类任务.
- 对各种培训和测试场景进行了模型性能的系统评估,包括多角度融合和生态图像的整合.
主要成果:
- 多角度图像融合增加了5.72%的物种识别精度和13.53%的阶层分类到属.
- 在分类和等级任务中,将生态图像纳入模型的性能进一步提高了13%.
- 格拉德-CAM可视化证实,该模型侧重于分类学诊断特征,与经典分类学保持一致.
结论:
- 整合不同的视角和生态背景,可显著提高自动化昆虫识别模型的性能和跨领域的适应性.
- 开发的框架为在各种现实场景中可靠的自动化昆虫识别提供了实际解决方案.
- 该模型将看不见的种类归类为更高的等级的能力在系统学,标本分类和家族遗传假设生成方面具有潜在的应用.
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