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.


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    Titel :

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


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abdul Majeed, Anwar P.P. (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Huang, Xiaowei (Herausgeber:in) / Nguyen, Anh (Herausgeber:in) / Chen, Wei (Herausgeber:in) / Kim, Ue-Hwan (Herausgeber:in) / Yang, Junqing (Autor:in) / Wu, Chengzhangzheng (Autor:in) / Liu, Taimingwang (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    29.11.2024


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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