Record Details

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

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Lightweight CNN Models for Product Defect Detection with Edge Computing in Manufacturing Industries
 
Creator Bonam, Janakiramaiah
Kondapalli, Sai Sudheer
V, Narasimha Prasad L
Marlapalli, Krishna
 
Subject Convolution neural network
Deep learning
Edge devices
Lightweight model
 
Description 418-425
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.
 
Date 2023-04-03T10:03:21Z
2023-04-03T10:03:21Z
2023-04
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61657
https://doi.org/10.56042/jsir.v82i04.72390
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(04) [April 2023]