Research on surface defects detection method and system in manufacturing processes based on the fusion of multi-scale features and semantic segmentation for intelligent manufacturing

被引:2
作者
Gong, Yu [1 ]
Liu, Mingzhou [1 ]
Wang, Xiaoqiao [1 ]
Liu, Conghu [2 ]
Hu, Jing [1 ]
机构
[1] Hefei Univ Technol, Sch Mech Engn, Hefei 230009, Peoples R China
[2] Suzhou Univ, Sch Mech & Elect Engn, Suzhou, Peoples R China
关键词
Intelligent manufacturing; quality control; surface defects detection; multi-scale features; semantic segmentation;
D O I
10.3233/JIFS-223041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-scale defect features, blurred edges and inability to locate geometric features have been the three key factors limiting the detection of surface defects on quality control system in the industrial manufacturing process. In this study, a method based on the fusion of multi-scale features and pixel-level semantic segmentation is proposed for the detection of surface defects. The proposed method firstly fuses multi-level feature maps to balance the expressiveness of multi-scale features, then adds a boundary refinement module to enhance the accurate inference of edge fine-grained, and finally adopts an en-decoder architecture to locate geometric features at the pixel-level for each type of defects, realizing intelligent detection of geometric features of end-to-end multi-scale defects on the surface of parts. We conduct experiments using the collected parts datasets to evaluate the effectiveness of our framework. The experimental results show that the proposed model achieves MIoU of 80.1%, the recognition accuracy reaches more than 95 %, and a detection rate of up to 29.64 FPS, demonstrating the advancement and effectiveness of the proposed method with less misclassification and superior generalization performance and has progress and effectiveness in detecting surface defects of multi-scale features. It provides a research idea for the subsequent realization of surface quality inspection in the manufacturing process system.
引用
收藏
页码:6463 / 6481
页数:19
相关论文
共 39 条
  • [1] Video Foreground Extraction Using Multi-View Receptive Field and EncoderDecoder DCNN for Traffic and Surveillance Applications
    Akilan, Thangarajah
    Wu, Q. M. Jonathan
    Zhang, Wandong
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (10) : 9478 - 9493
  • [2] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [3] The PASCAL Visual Object Classes Challenge: A Retrospective
    Everingham, Mark
    Eslami, S. M. Ali
    Van Gool, Luc
    Williams, Christopher K. I.
    Winn, John
    Zisserman, Andrew
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 111 (01) : 98 - 136
  • [4] A new approach for rotation-invariant and noise-resistant texture analysis and classification
    Feraidooni, Mohammad Mahdi
    Gharavian, Davood
    [J]. MACHINE VISION AND APPLICATIONS, 2018, 29 (03) : 455 - 466
  • [5] A deep-learning-based approach for fast and robust steel surface defects classification
    Fu, Guizhong
    Sun, Peize
    Zhu, Wenbin
    Yang, Jiangxin
    Cao, Yanlong
    Yang, Michael Ying
    Cao, Yanpeng
    [J]. OPTICS AND LASERS IN ENGINEERING, 2019, 121 : 397 - 405
  • [6] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [7] Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/CVPR.2018.00745, 10.1109/TPAMI.2019.2913372]
  • [8] Kagermann H., 2015, MANAGEMENT PERMANENT, P23, DOI [10.1007/978-3-658-05014-6_2, DOI 10.1007/978-3-658-05014-6_2]
  • [9] Design of industrial internet of things system based on machine learning and artificial intelligence technology
    Kun, Xu
    Wang, Zhiliang
    Zhou, Ziang
    Qi, Wang
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (02) : 2601 - 2611
  • [10] Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network
    Li, Jiangyun
    Su, Zhenfeng
    Geng, Jiahui
    Yin, Yixin
    [J]. IFAC PAPERSONLINE, 2018, 51 (21): : 76 - 81