A Generic Automated Surface Defect Detection Based on a Bilinear Model

被引:39
|
作者
Zhou, Fei [1 ]
Liu, Guihua [1 ]
Xu, Feng [1 ]
Deng, Hao [1 ]
机构
[1] Southwest Univ Sci & Technol, Sch Informat Engn, Mianyang 621010, Sichuan, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 15期
基金
中国国家自然科学基金;
关键词
automated surface inspection; D-VGG16; bilinear model; Grad-CAM; classification; localization;
D O I
10.3390/app9153159
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Surface defect detection for tube object based on automated optical Inspection
    Wu, Hsien-Huang
    He, Chang-Jhu
    ICAROB 2018: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS, 2018, : 453 - 456
  • [2] Fabric Surface Defect Detection Based on GMRF Model
    Xu, Yichen
    Meng, Fanwu
    Wang, Lizhong
    Zhang, Mingyi
    Wu, Changshuo
    PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21), 2021,
  • [3] Printed Surface Defect Detection Model Based on Positive Samples
    Xin Zihao
    Wang Hongyuan
    Qi Pengyu
    Du Weidong
    Zhang Ji
    Chen Fuhua
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 72 (03): : 5925 - 5938
  • [4] A wafer surface defect detection method built on generic object detection network
    Wang, Xinyu
    Jia, Xiaoli
    Jiang, Chuyi
    Jiang, Sanxin
    DIGITAL SIGNAL PROCESSING, 2022, 130
  • [5] Surface Defect Detection of Rolled Steel Based on Lightweight Model
    Zhou, Shunyong
    Zeng, Yalan
    Li, Sicheng
    Zhu, Hao
    Liu, Xue
    Zhang, Xin
    APPLIED SCIENCES-BASEL, 2022, 12 (17):
  • [6] Printed Surface Defect Detection Model Based on Positive Samples
    Zihao, Xin
    Hongyuan, Wang
    Pengyu, Qi
    Weidong, Du
    Ji, Zhang
    Fuhua, Chen
    Computers, Materials and Continua, 2022, 72 (03): : 5925 - 5938
  • [7] Wavelet-based principal component analysis applied to automated surface defect detection
    Lin, Hong-Dar
    Chung, Chung-Yu
    Lin, Wan-Ting
    COMPUTATIONAL METHODS AND APPLIED COMPUTING, 2008, : 245 - 250
  • [8] Defect detection of laminated surface in the automated fiber placement process based on improved CenterNet
    Wang X.
    Kang S.
    Zhu W.
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2021, 50 (10):
  • [9] Effective Defect Detection Method Based on Bilinear Texture Features for LGPs
    Hong, Libin
    Wu, Xianglei
    Zhou, Dibin
    Liu, Fuchang
    IEEE ACCESS, 2021, 9 (09): : 147958 - 147966
  • [10] Detection of Defect Proportion for Workpiece Surface Based on a Fusion Prediction Model
    Tao, Sikai
    Zhang, Ruixun
    Li, Yumeng
    2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021), 2021, : 1093 - 1098