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Lightweight CNN Models for Product Defect Detection with Edge Computing in Manufacturing Industries


Affiliations
1 Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, 520 007, Andhra Pradesh, India
2 Tejas Networks, Bengaluru, 560 100, Karnataka, India
3 Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, 500 043, Telangana, India
4 Computer Science and Engineering, Sir C R Reddy College of Engineering, Eluru, 534 007, Andhra Pradesh, India
 

Detecting product defects is one of the manufacturing industry's most essential processes in quality control. Human visual inspection for product defects is the traditional method employed in the industry. Nevertheless, it can be laborious, prone to human mistakes, and unreliable. Deep Learning-based Convolution Neural Networks (CNN) has been extensively used in fully automating product defect detection systems. However, real-time edge devices installed at the manufacturing site generally have limited computing capability and cannot run different CNN models. A lightweight CNN model is adopted in this scenario to find a balance between defect detection, model training time, memory consumption, computing time and efficiency. This work provides lightweight CNN models with transfer learning for product defect detection on fabric, surface, and casting datasets. We deployed the trained model to the NVIDIA Jetson Nano-kit edge device for detection speed with better simulation results in terms of accuracy, sensitivity rate, specificity rate, and F1 measure in the workplace context of the Manufacturing Industries.

Keywords

Convolution Neural Network, Deep Learning, Edge Devices, Lightweight Model.
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  • Lightweight CNN Models for Product Defect Detection with Edge Computing in Manufacturing Industries

Abstract Views: 57  |  PDF Views: 55

Authors

Janakiramaiah Bonam
Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, 520 007, Andhra Pradesh, India
Sai Sudheer Kondapalli
Tejas Networks, Bengaluru, 560 100, Karnataka, India
Narasimha Prasad L V
Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, 500 043, Telangana, India
Krishna Marlapalli
Computer Science and Engineering, Sir C R Reddy College of Engineering, Eluru, 534 007, Andhra Pradesh, India

Abstract


Detecting product defects is one of the manufacturing industry's most essential processes in quality control. Human visual inspection for product defects is the traditional method employed in the industry. Nevertheless, it can be laborious, prone to human mistakes, and unreliable. Deep Learning-based Convolution Neural Networks (CNN) has been extensively used in fully automating product defect detection systems. However, real-time edge devices installed at the manufacturing site generally have limited computing capability and cannot run different CNN models. A lightweight CNN model is adopted in this scenario to find a balance between defect detection, model training time, memory consumption, computing time and efficiency. This work provides lightweight CNN models with transfer learning for product defect detection on fabric, surface, and casting datasets. We deployed the trained model to the NVIDIA Jetson Nano-kit edge device for detection speed with better simulation results in terms of accuracy, sensitivity rate, specificity rate, and F1 measure in the workplace context of the Manufacturing Industries.

Keywords


Convolution Neural Network, Deep Learning, Edge Devices, Lightweight Model.

References