Quantum-Inspired Optimization Based Convolutional Neural Network for Automated Defect Detection in Knitted Fabrics

Paper in proceeding, 2025

The modern process of textile manufacturing needs automated defect detection of fabrics in the manufacturing process because the manual inspection is slow, inconsistent and subject to error. The latest advances in the deep learning industry have improved fabric inspection, however, the systems based on the Convolutional Neural Networks (CNNs) can give discontinuous and noisy segmentation outputs in reaction to illumination, texture, and fabric motion variations. The paper introduces a Quantum-Inspired Optimization-based Convolutional Neural Network (QIO-CNN) that will be applicable to overcome these limitations and assist in identifying defects in knitted fabrics in real-time. The suggested architecture is built upon CNN and QIO module to enhance pixel-level predictions using a Quadratic Unconstrained Binary Optimization (QUBO) formulation. The QIO module provides spatial smoothness and coherence between adjacent pixels and is a global filter on CNN-generated probability maps. The proposed model is tested on MATLAB/Simulink to evaluate the real-time feasibility of the proposed model on the industrial circular knitting data. The results of the experiments have revealed that the proposed approach has been able to reinforce intersection over union (IoU) and F1-score by an average of 4-5 percent relative to classical CNN approach, and at the same time, reduces false alarm rates by half. Visual inspection confirms the existence of more continuous defects and greater stability in changing lighting conditions. The results also validate the fact that QIO enhanced the spatial accuracy of deep learning results without increasing the complexity of the network.