8 research outputs found
A Novel Privacy Preserving Biometric Authentication Scheme Using Polynomial Time Key Algorithm In Cloud Computing
No full textCorrection to: Smart Cyclones: Creating Artificial Cyclones with Specific Intensity in the Dearth Situations Using IoT
No full textSmart Cyclones: Creating Artificial Cyclones with Specific Intensity in the Dearth Situations Using IoT
No full text
Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry
No full text178-184Surface defect identification is essential for maintaining and improving the quality of industrial products. However,
numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process,
considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can
detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of
expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a
Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on lowfrequency
edge devices, was proposed in this research. It provides sufficient feature extractions with little computational
overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data
augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model
was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The
experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while
reliably and robustly diagnosing them
MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection
No full text128-134In 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%
Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry
Get PDFSurface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them
MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection
Get PDFIn 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%
