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


Affiliations
1 Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India
2 University College of Engineering Kakinada (A), Jawaharlal Nehru Technological University Kakinada 533 003, Andhra Pradesh, India
3 Computer Science and Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru 521 356, Andhra Pradesh, India
4 Computer Science and Engineering, Sir C R Reddy College of Engineering, Eluru 534 007, Andhra Pradesh, India
5 Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522 302, Andhra Pradesh, India
 

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 resource-constrained 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%.

Keywords

Deep Learning, Edge Devices, Industrial IoT, Modeling, MobileNetV2.
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  • MobileNetV2-Based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection

Abstract Views: 111  |  PDF Views: 85

Authors

Lakshmi Ramani Burra
Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India
Karuna A
University College of Engineering Kakinada (A), Jawaharlal Nehru Technological University Kakinada 533 003, Andhra Pradesh, India
Srinivasarao Tumma
Computer Science and Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru 521 356, Andhra Pradesh, India
Krishna Marlapalli
Computer Science and Engineering, Sir C R Reddy College of Engineering, Eluru 534 007, Andhra Pradesh, India
Praveen Tumuluru
Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522 302, Andhra Pradesh, India

Abstract


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 resource-constrained 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%.

Keywords


Deep Learning, Edge Devices, Industrial IoT, Modeling, MobileNetV2.

References