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Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition.

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Titel: Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition.
Autoren: Huang, Liang, Shen, Qiongxia, Jiang, Chao, Yang, You
Quelle: Sensors (14248220); Oct2024, Vol. 24 Issue 20, p6752, 20p
Schlagwörter: ARTIFICIAL neural networks, FIELD programmable gate arrays, CIGARETTE filters, COMPUTER vision, ARTIFICIAL intelligence
Abstract: In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms. [ABSTRACT FROM AUTHOR]
ISSN:14248220
DOI:10.3390/s24206752