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MDE-DETR:多域增强功能融合算法,用于复杂果园中的海湾检测和计数

Cheng Zhou¹, Yuyu Zhang¹, Wei Fu²

¹School of Information Engineering, Huzhou University, Huzhou, China.

Frontiers in plant science

|December 15, 2025

概括

一个新的多域增强DETR (MDE-DETR) 算法改善了果园中的海湾检测. 这种高效,轻量级的解决方案提高了对小,封闭的目标的准确性,这对于产量预测至关重要.

科学领域:

计算机视觉计算机视觉
农业技术农业技术
机器学习机器学习

背景情况:

贝的检测对于准确的产量预测至关重要.
传统方法在复杂的果园环境中与小,封闭和密集分布的海目标作斗争.

研究的目的:

开发一种先进的检测算法,用于在具有挑战性的果园条件下预测海果产量.
为了提高检测小型和封闭的贝目标的准确性和效率.

主要方法:

提出了一个多域增强的DETR (MDE-DETR) 算法,其中包括一个增强的特征提取网络 (EFENet) 与多路径特征增强模块 (MFEM).
实施了多域特征融合网络 (MDFFN),包括SPDConv,跨阶段多核区块 (CMKBlock) 和双域关注,用于多规模的特征融合.
引入了自适应可变形采样 (ADSample) 模块,以提高对阻塞和密集目标分布的稳定性.

主要成果:

在贝数据集上,MDE-DETR实现了92.9%的mAP50和67.9%的mAP50:95,分别超过RT-DETR的3.8%和5.1%.
减少了25.76%的模型参数和25.14%的内存使用量,提供了一个高效和轻量级的解决方案.
在小目标 (VisDrone2019) 和密集封闭 (TomatoPlantfactoryDataset) 数据集上表现出强大的泛化.

结论:

在复杂的农业环境中,MDE-DETR算法提供了一种有效和高效的解决方案来检测海.
提出的方法显著改善了检测小,封闭和密集的目标,这对于精确农业和产量预测至关重要.

关键词:

贝贝利贝贝利贝贝利贝贝利贝贝贝利贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝贝复杂的环境复杂的环境深度学习 (deeplearning) 是一种深度学习.多级特征聚变的多级特征聚变对象检测检测对象检测对象检测

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