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Automated fabric defect detection using hybrid particle cat swarm optimizer with deep learning model

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Abstract

Fabric Defect (FD) detection has played an important role in the process of fabric production. FDs lead to a reduction in cloth prices, causing a significant loss of 45% to 65% for the cloth manufacturer. The detection of FDs becomes a challenging task in the fabric industry due to its complicated shapes and a considerable amount of FDs. Thus, a new detection technique, which has great detection speed and accuracy, is desired to replace manual work. With the emergence of Convolution Neural Networks (CNN) and the development of machine vision and Deep Learning (DL), various detection techniques, combining the benefits of DL and machine vision, have emerged, which replace manual and image processing approaches. The study presents an Automated Fabric Defect Detection using a Hybrid Particle Cat Swarm Optimizer with a Deep Learning (AFDD-HPCSODL) algorithm. AFDD-HPCSODL method aims to detect and categorize the existence of defects in fabric production. The objective is to automate and improve the performance of fabric defect detection, addressing the complexity of different defect patterns and characteristics in textile images. In the presented AFDD-HPCSODL technique, two stages of data pre-processing take place namely contrast enhancement and bilateral filtering (BF)-based noise elimination. Besides, a fusion of feature extraction processes is carried out using Inception V3 and EfficientNetB3. Moreover, the Attention Convolutional Long Short-Term Memory (ACLSTM) network is exploited for the detection and classification of FDs. Furthermore, the HPCSO technique carries out the hyperparameter selection of the ACLSTM model. Finally, the Root Mean Square Propagation (RMSProp) optimizer with the NestNet model is utilized for the defect segmentation process. The stimulation validation of the AFDD-HPCSODL method is studied on the two FD datasets. Widespread comparative results emphasized the improved performance of the AFDD-HPCSODL technique on FD classification with maximum accuracy of 97.92% and 98.89% on the Kaggle FD dataset and the ZJU-Leaper dataset, correspondingly.

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Authors and Affiliations

  1. Department of Computer and Information Science, Faculty of Science, Annamalai University, Chidambaram, India

    N. Sajitha

  2. Thiru A. Govindasamy Government Arts College, Tindivanam, 604307, India

    S. Prasanna Priya

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Sajitha, N., Priya, S.P. Automated fabric defect detection using hybrid particle cat swarm optimizer with deep learning model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18425-3

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