Automated leather defect inspection using statistical approach on image intensity

Leather is a very important raw material in many manufacturing industries. For example to produce footwear, garments, bags and accessories. Prior to the mass production of certain product, a professional leather visual inspection process for defection spotting is essential as the quality control step. However, to date, there is a lack of fully-automated leather inspection systems in the industry, whereby most manufacturers rely on experienced and trained experts to mark out the defects in the leather. This kind of human assessment work is inefficient and inconsistent. Therefore, this paper proposes a method that based on image processing techniques, namely, gray level histogram analysis, to detect defects of the leather. Specifically, the histogram characteristics such as the mean and standard deviation are extracted and treated as the features. Then, the statistical Kolmogorov–Smirnov’s two-sample test is utilized to perform feature selection. Followed by a thresholding method to reduce the dimensionality of the features. Finally, the features are categorized by several well-known classifiers. The best classification accuracy obtained are 99.16% and 77.13% on two different datasets respectively.