A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality

Casagrande, Luan; Macarini, Luiz Antonio Buschetto; Bitencourt, Daniel Pires; Fröhlich, Antônio Augusto; Araújo, Gustavo Medeiros de

doi:10.1007/s00138-020-01121-1

Cited by 11 publications

(1 citation statement)

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“…Although this algorithm retains the detailed information of defective images and facilitates defect detection, it has limited realtime applicability. Casagrande et al proposed a feature extraction method comprising segmentation-based fractal texture analysis and discrete wavelet transform [11]. This method accurately detects defects on single-color brick surfaces; however, its performance degrades in the case of textured tiles.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects in Ceramic Tiles With Complex Texture

Zhang

Peng

et al. 2021

IEEE Access

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To solve the problem of false defect detection owing to the interference of the texture attribute of ceramic tiles, a method for detecting surface defects in complex-textured ceramic tiles is proposed. Based on the visual detection principle of ceramic tile surfaces, an image acquisition system is established to obtain the ceramic tile image. After image segmentation and correction, the surface defects are preliminarily detected using a saliency detection method. Then, the image sub-block containing the defect area is cut out for secondary detection. The false defects are eliminated, and the final detection of the ceramic tile surface defects is completed using the defect determination method. The feasibility and effectiveness of the defect detection method are studied via comparative experiments. Experimental results show that the maximum accuracy rate of the proposed defect detection method is 98.75%, which satisfies the actual detection requirements and confirms the practical significance of the proposed method.INDEX TERMS Complex-textured ceramic tiles, defect determination, image acquisition, image sub-block, saliency detection HUAILIANG ZHANG received his Ph.D.

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Section: Introductionmentioning

confidence: 99%