通过深度学习在X射线图像中对种子缺陷进行强大和高通量检测
Sherif Hamdy1, Aurélie Charrier1, Laurence Le Corre1
1GEVES, Station Nationale d'Essais de Semences, 25 Georges Morel, 49070, Beaucouze, France.
Plant methods
|May 6, 2024
概括
这项研究介绍了X-Robustifier,这是使用二维X射线成像和深度学习精确检测种子缺陷的管道. 这种更快,更强大的方法在种子质量控制的速度和准确性方面超过了人类的表现.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 非破坏性测试 不破坏性测试
背景情况:
- 优化种子批量质量需要可靠检测内部种子缺陷.
- 非破坏性成像技术,特别是X射线成像,对于这个任务至关重要.
- 对3D断层扫描图像的深度学习显示出希望,但需要更快的方法.
研究的目的:
- 开发和验证一种基于深度学习的管道,用于使用二维X射线放射来检测种子缺陷.
- 创建一个更快,更强大的替代品,以3D断层扫描种子缺陷分析.
- 为了证明拟议的X-Robustifier管道的有效性.
主要方法:
- 获取有缺陷和无缺陷种子的二维X射线图像.
- 开发一种使用最先进的物体检测神经网络的深度学习模型.
- 实施特定的数据增强技术,以解决低缺陷比率和提高稳定性.
主要成果:
- 精确检测种子缺陷,F1得分超过90%.
- 在计算时间和错误率方面,性能超过了人类的能力.
- 对物理噪音,缩小维度和种子涂层等常见扭曲的证明强度.
结论:
- X-Robustifier管道可通过二维X射线成像实现自动检测种子缺陷.
- 这种在甜菜和豆豆上展示的方法可以扩展到其他物种.
- 这项研究推动了深度学习的常规应用,用于自动检测种子缺陷.
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