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Mirko Agarla¹, Paolo Napoletano¹, Raimondo Schettini¹

¹Dipartimento di Informatica, Sistemistica e Comunicazione, Università Milano-Bicocca, 20126 Milano, Italy.

Sensors (Basel, Switzerland)

|September 28, 2023

概括

本研究引入了用于自动化果缺陷细分的深度学习模型,提高了农业质量控制的准确性和效率. 该方法实现实时性能,并使用标准RGB图像显示可比结果.

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:

自动缺陷细分对于果质量控制和农业食品安全至关重要.
现有的方法往往缺乏实时应用所需的准确性或效率.

研究的目的:

开发一种深度学习模型,用于准确高效地对果缺陷进行自动细分.
通过数据合成和探索RGB图像输入来提高模型的性能和适用性.

主要方法:

使用了一种新的卷积神经网络 (CNN),具有U形架构和有针对性的跳过连接.
开发了一种特设数据合成技术,以增加数据集并减轻过度匹配.
该模型在多光谱果图像上进行了评估,并与通用细分架构进行了比较.

主要成果:

拟议的模型在细分精度方面显著优于现有的通用深度学习架构.
该方法证明了高计算效率,使实时 (GPU) 和准实时 (CPU) 视觉检查成为可能.
仅使用RGB图像的精度几乎可以与多光谱图像相提并论,从而提高了实际应用性.

结论:

开发的深度学习方法为农业环境中自动化果缺陷细分提供了卓越的解决方案.
该模型的实时功能和适应RGB图像的适应性使其适用于实用,大规模的视觉检查系统.
这项研究促进了水果行业的自动化质量控制,确保了更好的食品安全和产品质量.

关键词:

果缺陷细分的细分方法多光谱成像技术的使用.实时的深度学习.视觉检查视觉检查视觉检查视觉检查

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