VisionCrack: Smartphone-Based Fine Crack Width Measurement via Adaptive Scaling With Multithickness 1D Barcodes

Abstract

Accurate quantification of surface crack width is essential for maintaining civil infrastructure, yet conventional visual inspection is subjective and inconsistent. This study presents VisionCrack, a smartphone-based system for low-cost image-based crack width measurement using a 1D barcode. By analysing five predefined barcode bar thicknesses (T1–T5), VisionCrack estimates an adaptive scale (mm/pixel) using linear regression. Rather than relying on a single fixed reference, the method uses multiple barcode thickness references within each image to compensate for measurement bias. In experiments against crack width gauges, VisionCrack achieved a mean absolute error (MAE) of 0.0105 mm. Compared with a single-reference fixed-scale baseline representing prior marker-based approaches, the proposed method showed a lower error (0.0105 mm vs 0.1183 mm). These results demonstrate the calibration accuracy and practical potential of the proposed method under controlled image-capture conditions.