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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

Liang Huang¹, Qiongxia Shen², Chao Jiang²

¹School of Electronic Information and Communications, Huazhong University of Science & Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|October 26, 2024

Summary

This study introduces an AI model for cigarette defect detection, achieving 95.88% accuracy. The system uses RESNET18 and FPGA deployment for high-speed, real-time quality control in manufacturing.

Keywords:

deep neural network defect detection field programmable gate array real-time

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Last Updated: Jun 9, 2025

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Area of Science:

Manufacturing Technology
Artificial Intelligence
Computer Vision

Background:

Machine vision and AI are used in cigarette manufacturing for defect detection.
Current methods struggle with high accuracy and real-time detection for complex cigarette patterns.

Purpose of the Study:

To develop an accurate and high-speed defect detection model for cigarettes.
To address the limitations of existing real-time defect detection systems.

Main Methods:

A RESNET18-based model combined with a feature enhancement algorithm was proposed.
The model was deployed on a field-programmable gate array (FPGA) for parallel processing.

Main Results:

The proposed model achieved a detection accuracy of 95.88% on a cigarette filter defect dataset.
An end-to-end detection speed of 9.38 ms was achieved.

Conclusions:

The developed model effectively improves detection accuracy for cigarette defects.
FPGA deployment enables high-speed, real-time defect detection in cigarette manufacturing.