Large-Field-of-View Measurement of Fabric Density Based on Moiré Pattern for Industrial IoT

Abstract

Industrial quality inspection faces a fundamental trade-off between large-field-of-view (FOV) coverage and high precision. As a representative case, fabric density, a critical textile quality metric, directly affects fabric’s performance, with uncorrected deviations causing large-scale defects. Existing methods rely on close-range imaging to capture fine fabric details, failing to achieve both a large FOV and high precision simultaneously. In this paper, we propose MoiréDensimeter, a high-precision density measurement solution for real-time production, utilizing low-cost COTS cameras to enable large-FOV monitoring. By innovatively introducing moiré patterns as a visual medium, we transform the micron-scale high-frequency fabric textures captured at long distances into recognizable low-frequency moiré pattern, enabling high-precision density measurement in a large FOV. To bridge moiré features with fabric density, we develop a mathematical model and propose a clearness-adaptive density scaling strategy to extend the measurement range. To address complex industrial environmental interference, we propose a Demosaicing Feature Map (DFM) method based on high-frequency characteristics in moiré pattern generation, enabling precise fabric detection in large field of view images. Extensive experimental results show that MoiréDensimeter achieves a large-FOV measurement of sub-millimeter density at a maximum distance of 6.5 meters, with an average micron-level relative error of 0.245%.