果果边缘检测模型使用粗集和卷积神经网络
Junqing Li1, Ruiyi Han2, Fangyi Li3
1College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种使用深度学习和粗略集的新果果边缘检测模型 (RED). 红色模型提高了精度和稳定性,用于智能水果采摘和产量预测,即使在具有挑战性的条件下.
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:
- 精确的果检测对于智能采摘和产量预测至关重要.
- 现有的边缘检测方法与复杂的农业环境作斗争.
研究的目的:
- 为智能农业开发一种有效的果边检测算法.
- 为了提高果检测的准确性和稳定性.
主要方法:
- 提出了一个融合边缘检测模型 (RED),将卷积神经网络 (CNN) 和粗略集结合起来.
- 使用Faster R-CNN进行初始图像细分和K-means集群以减少噪音.
- 应用了粗略的集合理论,以改进不同照明和遮蔽条件下的边缘检测.
主要成果:
- 红色模型在检测果果边缘方面表现出高精度和稳定性.
- 与传统运营商相比,显著提高了检测准确性和稳定性.
- 在诸如复杂的背景和不同的照明等具有挑战性的条件下,性能尤其显著.
结论:
- 红色模型为智能水果采摘和产量预测提供了一个有前途的解决方案.
- 融合方法有效地解决了水果检测中的噪音和环境挑战.
- 这项研究为推进自动化农业实践提供了基础.
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