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DomAda-FruitDet:用于自动标签的域自适应式无水果检测模型

Wenli Zhang¹, Chao Zheng¹, Chenhuizi Wang¹

¹Information Department, Beijing University of Technology, Beijing 100022, China.

Plant phenomics (Washington, D.C.)

|January 26, 2024

概括

深度学习果实检测在数据标签方面面临挑战. 一个新的域自适应模型,DomAda-FruitDet,通过解决水果图像中的域间隙来提高自动标记的准确性.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
农业技术农业技术

背景情况:

深度学习在现代农业中显著推进了水果检测.
用于果实检测模型的手动数据标签是耗时和劳动密集的.
之前的自动标签方法在源果和目标果数据集之间存在域间隙.

研究的目的:

开发一种改进的自动标签方法来检测水果.
为了解决果实检测数据集中的域间隙问题.
为了提高智能果园系统的准确性.

主要方法:

提出了一个域自适应的无果实检测模型 (DomAda-FruitDet).
实现了前景域适应结构,具有双重预测层,用于多尺度检测.
利用基于样本分配的背景域适应策略来改善特征提取.

主要成果:

DomAda-FruitDet有效地减少了果实检测中的域差距.
在标记果 (90.9%),西红 (90.8%),皮塔亚 (88.3%) 和果 (94.0%) 数据集中实现了高平均精度.
显著提高了之前提出的水果自动标签方法的准确性.

结论:

拟议的DomAda-FruitDet模型成功克服了果实检测中的域间隙挑战.
这种方法提高了智能果园自动标签的效率和准确性.
这些发现有助于在水果行业更有效的深度学习应用.

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相关概念视频

Fruit Development, Structure, and Function

Fruit Development, Structure, and Function

Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.