淡江大學機構典藏:Item 987654321/119181
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    題名: SurfNetv2: An Improved Real-time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards
    作者: Chi-Yi Tsai;Hao-Wei Chen
    關鍵詞: deep learning;supervised end-to-end learning;surface defect recognition;SurfNet;calcium silicate boards
    日期: 2020-08-05
    上傳時間: 2020-09-23 12:11:08 (UTC+8)
    摘要: This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.
    關聯: Sensors 20(16), 4356
    DOI: 10.3390/s20164356
    顯示於類別:[電機工程學系暨研究所] 期刊論文

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