高斯-哈尔转换融合增强了石榴成熟度检测的DEIM
Yuying Wang1, Songzuo Liu2,3, Pingping Hao1
1Information Engineering College, Shandong Huayu University of Technology, Dezhou, 253000, China.
Scientific reports
|February 11, 2026
概括
这项研究介绍了GLMF-DEIM,这是一种用于自动检测石榴成熟度的新算法. 它在复杂的环境中提高了准确性,并为智能农业应用提供了高效的计算.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动化的石榴成熟度检测对于优化产量和降低成本至关重要.
- 现有的方法难以区分石榴和叶子,在自然环境中平衡精度和计算效率.
研究的目的:
- 开发一种轻量级的石榴成熟度检测算法,GLMF-DEIM,可以克服当前方法的局限性.
- 为了提高现实世界农业应用的检测精度和计算效率.
主要方法:
- GLMF-DEIM算法使用高斯-哈尔离散波纹转换模块进行特征分离.
- 它结合了轻量级的适应性重量下采样和轻量级的频域动态卷积阶段,以实现高效的特征提取.
- 采用多级特征融合网络和优化训练策略 (密度O2O匹配,匹配性意识损失).
主要成果:
- 在5855张图像的数据集上,GLMF-DEIM模型实现了高性能指标:93.1%AP50,84.5%AP75和32.7%APS.
- 与最佳基线相比,表现出1.9%,2.3%和1.3%的相对改善.
- 通过8.16M参数实现了低计算成本 (16.9 GFLOPs),表明了高效的推理.
结论:
- GLMF-DEIM有效地解决了目标背景相似性,并在准确性和效率之间实现了有利的权衡.
- 该算法非常适合在智能农业系统中进行边缘部署,以实时监控石榴.
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