Identification of overlapped wool/cashmere fibers based on multi-focus image fusion and convolutional neural network

ABSTRACT

Identification of cashmere and wool fibers has always been one of the essential topics in the field of textile. To solve this problem, this paper proposes anew overlapped fiber recognition method based on multi-focus image fusion and convolutional neural network. Firstly, the images of wool and cashmere fibers were captured by an optical microscope and adigital camera. Secondly, the fiber images with clear features were acquired by the multi-focus image fusion method. Then, image preprocessing operations were used to obtain single fiber images. Finally, the features of overlapped fibers are extracted and classified by the classical convolutional neural network. The results show that when the ratio of the training set to the test set is 8:2, the recognition accuracy of the method proposed in this paper reaches the highest of 98.7%, which can be used to realize the fast and accurate identification of overlapped fibers.

摘要:;

羊绒和羊毛纤维的鉴别一直是纺织领域的重要课题之一. 针对这一问题，提出了一种基于多聚焦图像融合和卷积神经网络的重叠光纤识别方法. 首先，利用光学显微镜和数码相机采集羊毛和羊绒纤维的图像. 其次，采用多聚焦图像融合方法获得特征清晰的纤维图像. 然后对图像进行预处理，得到单纤维图像. 最后，利用经典卷积神经网络对重叠光纤进行特征提取和分类. 结果表明，当训练集与测试集的比值为8:2时，该方法的识别准确率最高可达98.7%，可以实现对重叠光纤的快速准确识别.

KEYWORDS:

• Overlapped fiber
• Multi-Focus image fusion
• Wool
• Cashmere
• Convolutional neural network
• Fiber identification

Keywords:

• 关键词:;Overlapped fiber
• 重叠纤维
• Multi-focus image fusion
• 多聚焦图像融合
• Wool
• 羊毛
• Cashmere
• 羊绒
• Convolutional neural network
• 卷积神经网络
• Fiber identification光纤识别

Acknowledgments

This work was supported by the National Natural Science Foundation of China, under Grant 61876106, the Shanghai Natural Science Foundation of China, under Grant 18ZR1416600, the Zhihong Scholars Plan of Shanghai University of Engineering Science, under Grant 2018RC032017, and the Shanghai Local capacity-building projects, under Grant 19030501200.

Additional information

Funding

This work was supported by the the Zhihong Scholars Plan of Shanghai University of Engineering Science [2018RC032017]; the Shanghai Natural Science Foundation of China [18ZR1416600]; the Shanghai Local capacity-building projects [19030501200]; the National Natural Science Foundation of China [61876106].