Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

Yu Shih, Chien Chih Kuo, Ching Hung Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The recent trends in Industry 4.0 and Internet of Things have encouraged many factory managers to improve inspection processes to achieve automation and high detection rates. However, the corresponding cost results of sample tests are still used for quality control. A low-cost automated optical inspection system that can be integrated with production lines to fully inspect products without adjustments is introduced herein. The corresponding mechanism design enables each product to maintain a fixed position and orientation during inspection to accelerate the inspection process. The proposed system combines image recognition and deep learning to measure the dimensions of the thread and identify its defects within 20 s, which is lower than the production-line productivity per 30 s. In addition, the system is designed to be used for monitoring production lines and equipment status. The dimensional tolerance of the proposed system reaches 0.012 mm, and a 100% accuracy is achieved in terms of the defect resolution. In addition, an attention-based visualization approach is utilized to verify the rationale for the use of the convolutional neural network model and identify the location of thread defects.

Original languageEnglish
Pages (from-to)2087-2099
Number of pages13
JournalIntelligent Automation and Soft Computing
Issue number2
StatePublished - 2023


  • Attention
  • Automated optical inspection
  • Deep learning
  • Real-time inspection


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