超光谱成像和多模块联合等级残余网络在种子棉花外来纤维识别中的应用
Yunlong Zhang1, Laigang Zhang1, Zhijun Guo2
1School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究引入了一种使用高光谱成像和深度学习网络的新方法,以准确识别种子棉花中的外来纤维. 先进的技术显著提高了各种纤维类型和尺寸的检测精度.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 织工程 织工程 织工程
背景情况:
- 使用RGB图像在种子棉花中区分透明和白色的外来纤维具有挑战性.
- 现有的深度学习 (DL) 算法在种子棉花中难以识别多类外来纤维.
研究的目的:
- 为了提高DL算法的识别精度,用于种子棉花中的外来纤维.
- 开发一种可靠的方法来识别不同尺寸的白色,透明和混合的外来纤维.
主要方法:
- 结合高光谱成像技术与多模块联合层次残留网络 (MJHResNet).
- 应用预处理技术对高光谱图像 (HSI) 进行噪声降低.
- 设计了一个双层次的残余 (DHR) 结构,用于多层次的信息获取和梯度稳定性.
- 集成了一个挤压和激发网络 (SENet),以优化特征表达和减少冗余信息.
主要成果:
- 在外国纤维识别中实现了98.71%的平均准确性和99.28%的整体准确性.
- 通过实验分析证明了与高级分类器相比的显著优势.
- 拟议的方法有效地处理多类外来纤维,包括不同尺寸的白色和透明类型.
结论:
- 开发的超光谱成像和MJHResNet方法为种子棉花中的外来纤维识别提供了高度准确的解决方案.
- 这种方法在织工业中具有很大的实践应用潜力,用于质量控制.
- 整合了DHR结构和SENet模块,提高了模型在复杂的识别任务中的性能.
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