Open Access

ARTICLE

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

Yu Shih, Chien-Chih Kuo, Ching-Hung Lee^*

Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan

* Corresponding Author: Ching-Hung Lee. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2087-2099. https://doi.org/10.32604/iasc.2023.027659

Received 23 January 2022; Accepted 19 April 2022; Issue published 19 July 2022

Abstract

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.

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Keywords

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