Surface defects in manufacturing processes pose significant challenges, affecting product quality and safety. Traditional labour-based inspection is deemed to be ineffective and has led the shift to computer vision-based solutions and to a certain extent, the employment of artificial intelligence. In the present study, we leverage the capability of a pre-trained convolutional neural networks model, i.e. VGG19, in extracting the features from a set of surface defect dataset that comprises six unique defect categories. The ability of different machine learning models, namely Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbour (kNN) and Support Vector Machine (SVM), to classify the defects was investigated. It was demonstrated from the study that the VGG-19 + LR combination is the optimal pipeline. This study suggests that the feature-based transfer learning approach is an attractive approach to be employed for surface defect detection.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Surface Defect Detection: An Approach Utilising Feature-Based Transfer Learning


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023



    Publication date :

    2024-11-29


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Rail surface defect detection based on deep learning

    Li, Xiaoqing / Zhou, Ying / Chen, Hu | SPIE | 2020


    Filter-based feature selection for rail defect detection

    Mandriota, C. / Nitti, M. / Ancona, N. et al. | British Library Online Contents | 2004


    Page Segmentation and Classification Utilising Bottom-Up Approach

    Drivas, D. / Amin, A. | British Library Conference Proceedings | 1995


    Train positioning system based on sleeper defect feature detection

    WU SONGRONG / ZHENG YINGJIE / HU JIEYU et al. | European Patent Office | 2020

    Free access

    Railway Track Defect Detection using Transfer Learning With EfficientNetB3

    Lodhi, Sachin / Sakshi / Kukreja, Vinay | IEEE | 2022