1. Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition
- Author
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Liang Huang, Qiongxia Shen, Chao Jiang, and You Yang
- Subjects
defect detection ,deep neural network ,field programmable gate array ,real-time ,Chemical technology ,TP1-1185 - 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. more...
- Published
- 2024
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