SE-COTR:一种新的果实细分模型,用于绿果在复杂果园中的应用
Zhifen Wang1, Zhonghua Zhang1, Yuqi Lu1
1School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
Plant phenomics (Washington, D.C.)
|June 2, 2023
概括
一种新的深度学习方法,SE-COTR,准确地对复杂果园中的绿果进行细分. 这种创新方法提高了果实检测,即使是小或封闭的目标,使有效的农业应用.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于非结构化的环境,在自然果园中有效地检测和细分绿色水果是具有挑战性的.
- 现有的方法在不同的水果大小,遮蔽和类似的背景颜色方面扎.
研究的目的:
- 提出一种创新的深度学习方法,SE-COTR (基于坐标变压器的细分),用于准确和实时的绿果细分.
- 增强特征重点并集成多尺度特征,以在复杂的果园条件下提高细分性能.
主要方法:
- 使用MobileNetV2作为一个轻量级的骨干.
- 开发了一个基于注意力的坐标坐标变压器模块来增强特征焦点.
- 实施了一个联合的金字塔上模块,用于多尺度的功能集成.
- 应用动态卷积,例如面具预测.
主要成果:
- 在复杂的果园中,SE-COTR实现了61.6%的平均平均精度,用于绿果的细分,这些果园具有遮和不同尺度.
- 小目标水果的细分精度达到了43.3%,超过了先进模型.
- 该方法在具有挑战性的环境中表现出低复杂性和有效性.
结论:
- 在绿色水果细分中,SE-COTR有效地解决了低准确度和模型复杂性的问题.
- 该模型可以部署在便携式设备上,用于在复杂的果园中执行准确和高效的农业任务.
- 拟议的方法为智能农业和自动化水果收获提供了重大进展.
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