Semantic Segmentation Using DeepLabv3+ Model for Fabric Defect Detection

作     者：ZHU Runhu XIN Binjie DENG Na FAN Mingzhu ZHU Runhu;XIN Binjie;DENG Na;FAN Mingzhu

作者机构：School of Electronic and Electrical EngineeringShanghai University of Engineering ScienceShanghai 201620China School of Textile and Fashion TechnologyShanghai University of Engineering ScienceShanghai 201620China 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2022年第27卷第6期

页      面：539-549页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(61876106) Shanghai Local Capacity-Building Project(19030501200) 

主　　题：fabric defect detection semantic segmentation deep learning DeepLabv3+ 

摘      要：Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of corresponding features to complete the detection. To overcome the limitations of traditional algorithms, deep learning-based correlative algorithms can extract more complex image features and perform better in image classification and object detection. A pixel-level defect segmentation methodology using DeepLabv3+, a classical semantic segmentation network, is proposed in this paper. Based on ResNet-18,ResNet-50 and Mobilenetv2, three DeepLabv3+ networks are constructed, which are trained and tested from data sets produced by capturing or publicizing images. The experimental results show that the performance of three DeepLabv3+ networks is close to one another on the four indicators proposed(Precision, Recall, F1-score and Accuracy), proving them to achieve defect detection and semantic segmentation, which provide new ideas and technical support for fabric defect detection.