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Research on Fabric Defect Detection Based on Deep Fusion DenseNet-SSD Network

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Gengzhe ZhengAuthors Info & Claims

icWCSN '20: Proceedings of the 2020 International Conference on Wireless Communication and Sensor Networks

Pages 60 - 64

https://doi.org/10.1145/3411201.3411701

Published: 26 August 2020 Publication History

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Abstract

Defect detection to control the quality of fabrics is one of the key tasks in the production process of fabrics. Although significant progress has been made in the research of fabric defect detection, while traditional methods are still difficult to cope with complex and variable defect shapes. In order to solve these problems, this paper proposes an adaptive fabric defect detection method based on DenseNet-SSD algorithm to improve the performance of fabric defect detection. This method uses the DenseNet network to replace the backbone network VGG16 in the SSD algorithm, which strengthens the transfer between feature maps, alleviates the problem of gradient disappearance and reduces the number of network parameters. Compared with SSD, it improves network detection accuracy and real-time performance. The accuracy in the test set is 78.6mAP and the detection speed is 61FPS.

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Cited By

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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
Rocha DPinto LMachado JSoares FCarvalho V(2023)Using Object Detection Technology to Identify Defects in Clothing for Blind PeopleSensors10.3390/s2309438123:9(4381)Online publication date: 28-Apr-2023
https://doi.org/10.3390/s23094381

Index Terms

Research on Fabric Defect Detection Based on Deep Fusion DenseNet-SSD Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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icWCSN '20: Proceedings of the 2020 International Conference on Wireless Communication and Sensor Networks

May 2020

71 pages

ISBN:9781450377638

DOI:10.1145/3411201

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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AGH University of Science and Technology: AGH University of Science and Technology

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Published: 26 August 2020

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icWCSN 2020: 2020 International Conference on Wireless Communication and Sensor Networks

May 13 - 15, 2020

Warsaw, Poland

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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
Rocha DPinto LMachado JSoares FCarvalho V(2023)Using Object Detection Technology to Identify Defects in Clothing for Blind PeopleSensors10.3390/s2309438123:9(4381)Online publication date: 28-Apr-2023
https://doi.org/10.3390/s23094381

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Fabric defect detection algorithm based on YOLOv3 Transfer learning

Fabric Defects Detection based on SSD

Fabric Defect Detection Based on Projected Transform for Feature Extraction

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Fabric defect detection algorithm based on YOLOv3 Transfer learning

Fabric Defects Detection based on SSD

Fabric Defect Detection Based on Projected Transform for Feature Extraction

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