Automated Measurement of Smartphone Screen Scratches Using Visual Methods

Abstract

Abstract Quantitative measurement of smartphone screen scratches is crucial for pricing in the used smartphone market. Traditional manual visual inspection methods suffer from inherent limitations, namely being labor-intensive, subjective, and prone to inaccuracy. Hence, this study proposes a vision-based measurement method as a viable solution to overcome these challenges. The algorithm uses the Hessian enhancement to extract scratch features, applies adaptive thresholding to distinguish features from the background, and employs morphological reconstruction to reconstruct complete scratches. The topological analysis splits and mergers intersecting scratches, enabling individual segmentation. Finally, four metrics for measuring screen scratches include length, brightness, contrast, and maximum width to quantitatively characterize the damage of screen scratches. Experiments showed that the proposed algorithm outperforms other vision-based methods, with an accuracy of 99.6% in estimating the scratch length and a running time of 43.7 ms, which fully meets the efficiency and accuracy requirements of industrial application.