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Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

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Field Value
 
Title Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry
 
Creator D, Shobha Rani
Burra, Lakshmi Ramani
G, Kalyani
B, Narendra Kumar Rao
 
Subject CondenseNet
Convolutional neural networks
Deep learning
Edge device
Industrial products
 
Description 178-184
Surface 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.
 
Date 2023-02-08T05:35:21Z
2023-02-08T05:35:21Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61368
https://doi.org/10.56042/jsir.v82i2.69945
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]