research-article

Fabric Defects Detection based on SSD

Authors:

Yan DongAuthors Info & Claims

ICGSP '18: Proceedings of the 2nd International Conference on Graphics and Signal Processing

Pages 74 - 78

https://doi.org/10.1145/3282286.3282300

Published: 06 October 2018 Publication History

Abstract

In this paper, Fabric defect detection is a challenging task because of the complex texture. Deep learning technology provide a promising solution. As a kind of deep learning object detection model. Single Shot Multibox Detector(SSD)achieves good detection performance. However, the original SSD model may fail to detect the small objects. In this paper, we proposed a novel SSD model for fabric defect detection. Experimental results showed that the improved SSD model can accurately detect the defect region.

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Cited By

Guo PLiu YWu YGong RLi Y(2024)Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research ProgressIEEE Access10.1109/ACCESS.2024.339605312(63777-63808)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3396053
Lv HZhang HWang MXu JLi XLiu C(2024)Hyperspectral Imaging Based Nonwoven Fabric Defect Detection Method Using LL-YOLOv5IEEE Access10.1109/ACCESS.2024.337873912(41988-41998)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3378739
Guder OIsik SAnagun Y(2024)Fabric defects identification for textile industry with a deep learning approachThe Journal of The Textile Institute10.1080/00405000.2024.2383799(1-10)Online publication date: 5-Aug-2024
https://doi.org/10.1080/00405000.2024.2383799
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Index Terms

Fabric Defects Detection based on SSD
1. Computing methodologies

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ICGSP '18: Proceedings of the 2nd International Conference on Graphics and Signal Processing

October 2018

119 pages

ISBN:9781450363860

DOI:10.1145/3282286

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Griffith University
City University of Hong Kong: City University of Hong Kong

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New York, NY, United States

Publication History

Published: 06 October 2018

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ICGSP'18

ICGSP'18: The 2nd International Conference on Graphics and Signal Processing

October 6 - 8, 2018

NSW, Sydney, Australia

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Cited By

Guo PLiu YWu YGong RLi Y(2024)Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research ProgressIEEE Access10.1109/ACCESS.2024.339605312(63777-63808)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3396053
Lv HZhang HWang MXu JLi XLiu C(2024)Hyperspectral Imaging Based Nonwoven Fabric Defect Detection Method Using LL-YOLOv5IEEE Access10.1109/ACCESS.2024.337873912(41988-41998)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3378739
Guder OIsik SAnagun Y(2024)Fabric defects identification for textile industry with a deep learning approachThe Journal of The Textile Institute10.1080/00405000.2024.2383799(1-10)Online publication date: 5-Aug-2024
https://doi.org/10.1080/00405000.2024.2383799
Das Gupta ASadek ZShakhawat Hossain MToha TMondol AHabiba SMominul Alam S(2024)An Approach to Automatic Fault Detection in 4 Point System for Knitted Fabric With Our Benchmark Dataset Isl-KnitHeliyon10.1016/j.heliyon.2024.e35931(e35931)Online publication date: Aug-2024
https://doi.org/10.1016/j.heliyon.2024.e35931
Su ZZhang HLi PZhang HLu Y(2023)A lightweight model for digital printing fabric defect detection based on YOLOXJournal of Engineered Fibers and Fabrics10.1177/1558925023120870218Online publication date: 31-Oct-2023
https://doi.org/10.1177/15589250231208702
Yu ZXu YWang YSheng XXie G(2023)Dual-path segmentation network for automatic fabric defect detectionTextile Research Journal10.1177/0040517523119205793:23-24(5224-5236)Online publication date: 26-Aug-2023
https://doi.org/10.1177/00405175231192057
Liu ZTian BLi CDing SXi J(2023)CACFNet: Fabric defect detection via context-aware attention cascaded feedback networkTextile Research Journal10.1177/0040517523115143993:13-14(3036-3055)Online publication date: 27-Jan-2023
https://doi.org/10.1177/00405175231151439
Venkatachalam SKrishnamoorthy KSrilla JSenthilKumar SVelusamy SD S(2023)Deep Learning - Based Farm Disturbance Bird Detection2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331906(318-324)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331906
Chaka KShiferaw ASharew S(2023)Inspection of Cotton Woven Fabrics Produced by Ethiopian Textile Factories Through a Real-Time Vision-Based SystemJournal of Natural Fibers10.1080/15440478.2023.228661520:2Online publication date: 28-Nov-2023
https://doi.org/10.1080/15440478.2023.2286615
Lu BHuang B(2023)A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task trainingJournal of Intelligent Manufacturing10.1007/s10845-023-02105-435:3(1267-1280)Online publication date: 29-Mar-2023
https://doi.org/10.1007/s10845-023-02105-4
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