Wavelet-based Defect Detection System for Grey-level Texture Images

Abstract

In this study, a new wavelet-based approach (system) to the detection of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete wavelet transform (DWT) and generates statistically-based parameterized defect detection criteria. The introduced system's parameter provides the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The developed defect detection system was implemented using discrete Haar and Le Gall wavelet transforms. For the experimental part, samples of ceramic tiles, as well as glass samples, taken from real factory environment, were used.

Vision Transformers for Anomaly Detection and Localisation in Leather Surface Defect Classification Based on Low-Resolution Images and a Small Dataset

“…Image processing methods can generally be classified under the following four categories: (1) statistical algorithms such as a features-based wavelet method [7], (2) structural algorithms such as local binary patterns (LBP) [8] or Gabor filters [9], (3) spectral algorithms such as the wavelet transform [10], and (4) model-based algorithms, as can be found in in the work of Wang et al [11]. Comprehensive investigations conducted in the fields of automated visual defect detection for flat steel surfaces [12] and fabric defect detection in textile manufacturing [13] present a succinct compilation of the diverse methodologies utilized in surface defect detection.…”