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Saliency-based fabric defect detection via bag-of-words model

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Abstract

Fabric defect detection is a very crucial and challenging problem. A novel saliency-based fabric defect detection via bag-of-words model framework is suggested to solve this problem. First, the suggested saliency technique is used to get saliency maps from training images. The bag-of-words model is then developed using the K-means algorithm. Following that, the histogram of the visual vector is used to represent the image. Finally, these feature vectors are utilized to train a K-nearest neighbor method classifier to differentiate images with defected regions from those without. Images of defected and non-defected fabrics are stored in a database. The saliency maps are extracted using suggested and cutting-edge techniques. The proposed framework is trained and tested on the 70% and 30% rule. According to simulation results, the suggested framework using the suggested saliency detection technique achieves promising results on our current data collection.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Maria Kanwal, Syed Sohaib Ali & Abdul Ghafoor

  2. COMSATS University, Islamabad, Pakistan

    Muhammad Mohsin Riaz

Authors
  1. Maria Kanwal
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  2. Muhammad Mohsin Riaz
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  3. Syed Sohaib Ali
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  4. Abdul Ghafoor
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Contributions

MK conceived the idea and performed the simulation. MMR provided the mathematical formulation. SSA supervised the simulation, while AG supervised the whole research.

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Correspondence to Abdul Ghafoor.

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Kanwal, M., Riaz, M.M., Ali, S.S. et al. Saliency-based fabric defect detection via bag-of-words model. SIViP 17, 1687–1693 (2023). https://doi.org/10.1007/s11760-022-02379-w

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