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MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection

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Title MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection
 
Creator Burra, Lakshmi Ramani
A, Karuna
Tumma, Srinivasarao
Marlapalli, Krishna
Tumuluru, Praveen
 
Subject Deep learning
Edge devices
Industrial IoT
Modeling
MobileNetV2
 
Description 128-134
In textile manufacturing, fabric defect detection is an essential quality control step and a challenging task. Earlier,
manual efforts were applied to detect defects in fabric production. Human exhaustion, time consumption, and lack of
concentration are the main problems in the manual defect detection process. Machine vision systems based on deep learning
play a vital role in the Industrial Internet of things (IIoT) and fully automated production processes. Deep learning centered
on Convolution Neural Network (CNN) models have been commonly used in fabric defect detection, but most of these
models require high computing resources. This work presents a lightweight MobileNetV2-based Transfer Learning model to
assist defect detection with low power consumption, low latency, easy upgrade, more efficiency, and an automatic visual
inspection system with edge computing. Firstly, different image transformation techniques were performed as data
augmentation on four fabric datasets for the model's adaptability in various fabrics. Secondly, fine-tuning hyperparameters
of the MobileNetV2 with transfer learning gives a lightweight, adaptable and scalable model that suits the resourceconstrained
edge device. Finally, deploy the trained model to the NVIDIA Jetson Nano-kit edge device to make its detection
faster. We assessed the model based on its accuracy, sensitivity rate, specificity rate, and F1 measure. The numerical
simulation reveals that the model accuracy is 96.52%, precision is 96.52%, recall is 96.75%, and F1-Score is 96.52%.
 
Date 2023-01-16T10:00:04Z
2023-01-16T10:00:04Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61197
https://doi.org/10.56042/jsir.v82i1.69928
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]