基于卷积神经网络的织物图像识别方法研究进展

doi:10.19398/j.att.202110050

摘要/Abstract

摘要： 基于卷积神经网络的织物图像识别技术具有自主学习、准确率高和适用性广的优点,已被广泛应用于纺织工业生产中。本文面向纺织领域详细综述了基于卷积神经网络的疵点分类、瑕疵检测、图像分类、图像分割技术的具体应用和研究进展,总结了各种网络结构的改进点和优缺点,同时就目前存在的问题及未来研究方向进行了展望。随着网络结构的不断优化,图像识别方法对实时性和精确性各有侧重,但其鲁棒性和适用性仍有较大的发展空间,有待做进一步研究。

关键词: 卷积神经网络, 图像识别, 疵点分类, 瑕疵检测, 图像分类

Abstract: Fabric image recognition technology based on convolutional neural network has the advantages of autonomous learning, high accuracy and wide applicability, and has been widely used in textile industry production. This paper reviews the specific applications and research progress of defect classification, defect detection, image classification and image segmentation technology based on convolutional neural networks in the textile field. This article summarizes the improvement points, advantages and disadvantages of various network structures, and at the same time looks forward to the future research directions regarding the current problems. With the continuous optimization of network structure, image recognition methods have their own emphasis on real-time and accuracy, but their robustness and applicability still have a lot of room for development, and further research is needed.

Key words: convolutional neural network, image identification, defect classification, defect detection, image classification

中图分类号:

TS107
TP183

郑雨婷, 王成群, 陈亮亮, 吴江, 吕文涛. 基于卷积神经网络的织物图像识别方法研究进展[J]. 现代纺织技术, 2022, 30(5): 1-11.

ZHENG Yuting, WANG Chengqun, CHEN Liangliang, WU Jiang, LÜ Wentao. Research progress of fabric image processing methods based on convolutional neural network[J]. Advanced Textile Technology, 2022, 30(5): 1-11.

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