TopoGeoFusion:将基于对象拓的特征计算方法集成到几何特征分析中,以提高分类性能
N Shobha Rani1, Keshav Shesha Sai2, B R Pushpa2
1Department of Artificial Intelligence and Data Science, Gitam School of Technology, Bengaluru, GITAM (Deemed to be University), India.
MethodsX
|August 5, 2024
概括
这项研究开发了一种计算机视觉方法,使用智能手机图像自动将果分类为优质,标准或拒绝等级. 在TopoGeoFusion技术准确地评估水果的质量,即使有重叠的水果.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 图像处理 图像处理
背景情况:
- 自动化水果质量评估对于市场适用性和智能摄像机应用至关重要.
- 传统的分级方法可能是劳动密集型和主观的.
- 计算机视觉为客观和高效的水果分类提供了一个潜在的解决方案.
研究的目的:
- 开发一种自动化系统,使用智能手机拍摄的图像对子进行分类.
- 为准确评估,解决分离接触或重叠水果的挑战.
- 引入和评估质量和成熟度评估的"TopoGeoFusion"方法.
主要方法:
- 使用智能手机拍摄的1697个印度明星果的RGB图像.
- 开发了一种新的"TopoGeoFusion"方法,结合了几何和拓意识的特征.
- 使用多个分类器,包括随机森林,SVM,天真贝叶,决策树和KNN.
- 实现了果实检测和提取的细分模型,处理封闭的物体.
主要成果:
- 在使用随机森林与融合特征模型将果分为拒绝类,标准类和高级类的分类中实现了100%的准确性.
- 细分模型证明了可靠的果实检测和提取,平均mAP为0.56.
- 托波地质融合方法在评估等级和成熟度方面被证明是有效的,即使有重叠的果实.
结论:
- 托波GeoFusion有效地使用拓特征自动化果分类.
- 开发的模型是非常准确和可靠的,即使在复杂的场景与封闭的果实.
- 该技术可以从部分对象中进行检测和特征计算,从而增强分级功能.
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