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Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural

Jian Huang1, Liu Guixiong1, Binyuan He1

  • 1School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong Province, China.

Plos One
|February 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized deep learning model for machine vision inspection, significantly reducing processing time for online semantic segmentation in complex backgrounds. The enhanced model enables faster, parameter-efficient visual detection, suitable for mobile devices.

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Machine vision is crucial for intelligent manufacturing, particularly in quality inspection and precision production lines.
  • Online semantic segmentation under complex backgrounds presents a significant challenge in current machine vision applications.

Purpose of the Study:

  • To develop an optimized deep learning model for online semantic segmentation in machine vision inspection.
  • To improve the efficiency and parameter optimization of atrous convolution architectures for complex background segmentation.

Main Methods:

  • Utilized Atrous Spatial Pyramid Pooling (ASPP) and Residual Network (ResNet) as base architectures.
  • Proposed five modified ResNet residual building blocks to enhance image feature utilization.
  • Implemented fewer-parameters optimization for atrous convolution architecture.

Main Results:

  • Achieved a significant decrease in segmentation time (Tseg) from 719 ms to 296 ms (58.8% reduction).
  • Maintained high accuracy with only a minor decrease in intersection-over-union (IoU) from 86.7% to 86.6%.
  • Demonstrated applicability through successful segmentation of Chinese Yuan (CNY) currency.

Conclusions:

  • The proposed semantic segmentation model effectively reduces detection time while ensuring accuracy.
  • The fewer-parameters optimization makes neural network detection feasible on mobile terminals.
  • The method offers a significant advancement for real-time visual detection in intelligent manufacturing.