• Laser & Optoelectronics Progress
  • Vol. 56, Issue 21, 211003 (2019)
Xiaolei Li1, Shuguang Zeng2、*, Sheng Zheng2, Yanshan Xiao2, Shaowei Zhang2, and Qiang Li2
Author Affiliations
  • 1College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, Hubei 443002, China;
  • 2College of Science, China Three Gorges University, Yichang, Hubei 443002, China
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    DOI: 10.3788/LOP56.211003 Cite this Article Set citation alerts
    Xiaolei Li, Shuguang Zeng, Sheng Zheng, Yanshan Xiao, Shaowei Zhang, Qiang Li. Surface Crack Detection of Ceramic Tile Based on Sliding Filter and Automatic Region Growth[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211003 Copy Citation Text show less
    System design
    Fig. 1. System design
    Original images of ceramic tile. (a) Crack in the tile head; (b) crack in the texture region
    Fig. 2. Original images of ceramic tile. (a) Crack in the tile head; (b) crack in the texture region
    Block diagram of detection algorithm. (a) Image preprocessing; (b) crack detection; (c) crack determination
    Fig. 3. Block diagram of detection algorithm. (a) Image preprocessing; (b) crack detection; (c) crack determination
    Image preprocessing of tile head area and texture area. (a) Original images; (b) red channel images; (c) median filtered images
    Fig. 4. Image preprocessing of tile head area and texture area. (a) Original images; (b) red channel images; (c) median filtered images
    Defect detection for tile head area. (a) Filter template; (b) filtered image; (c) filter detection result
    Fig. 5. Defect detection for tile head area. (a) Filter template; (b) filtered image; (c) filter detection result
    Defect detection for texture area. (a) Seed point selection; (b) automatic region growth
    Fig. 6. Defect detection for texture area. (a) Seed point selection; (b) automatic region growth
    Detection results. (a) Morphological processing image of the tile head; (b) morphological processing image of the texture region
    Fig. 7. Detection results. (a) Morphological processing image of the tile head; (b) morphological processing image of the texture region
    Detection effects of different algorithms for tile head area. (a) Original image of tile head area; (b) maximum entropy; (c) sobel; (d) discrete wavelet transform; (e) proposed method
    Fig. 8. Detection effects of different algorithms for tile head area. (a) Original image of tile head area; (b) maximum entropy; (c) sobel; (d) discrete wavelet transform; (e) proposed method
    Detection effects of different algorithms for texture regions. (a) Original image of texture region; (b) maximum entropy; (c) sobel; (d) discrete wavelet transform; (e) proposed method
    Fig. 9. Detection effects of different algorithms for texture regions. (a) Original image of texture region; (b) maximum entropy; (c) sobel; (d) discrete wavelet transform; (e) proposed method
    AlgorithmNumber of errorsAccuracy /%
    Maximum entropy1288
    Sobel2377
    Discrete wavelet transform1189
    Proposed method694
    Table 1. Detection accuracy of four algorithms
    Xiaolei Li, Shuguang Zeng, Sheng Zheng, Yanshan Xiao, Shaowei Zhang, Qiang Li. Surface Crack Detection of Ceramic Tile Based on Sliding Filter and Automatic Region Growth[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211003
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