FAST DEFECT DETECTION FOR GLASS BOTTLE USING AUTOENCODER AND ERROR THRESHOLD

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Publication Details

Author list: Hanskunatai A.; Jaiyen S.; Claypo N.

Publisher: ICIC International

Publication year: 2024

Volume number: 18

Issue number: 6

Start page: 551

End page: 562

Number of pages: 12

ISSN: 1881-803X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195691090&doi=10.24507%2ficicel.18.06.551&partnerID=40&md5=1ab854110dcebf723b2c12d03c6baa7d

Languages: English-Great Britain (EN-GB)

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Abstract

Glass bottle defect detection is an important part of quality control process in any glass manufacturing industry. The bottles must be inspected before packaging. Machine vision for glass bottle defect detection is the technology and method to inspect and analyze the defects for images automatically. Machine vision requires high-ability method to detect the defect and reject the bottle with the defect quickly. In this paper, defect detection framework for glass bottle defect detection tasks using autoencoders and error threshold is proposed. The fast detection method, a small autoencoder neural network architecture was designed with only good bottle images to train an autoencoder neural network. The decoded images are representations of normal bottle images and calculate threshold errors value. Defect detection is done by comparing the error between the normal background image and the encoded images to a threshold error from the training set. The performance of our method was compared to several other methods: VGG16, MobileNetV3, ADA, edge detection and image threshold. The experimental results show that our method yields 80% of accuracy on the body dataset and 92% of accuracy on the neck dataset. The average training time of our method is faster than that of all other neural network-based methods. From the experimental results, we can conclude that our defect detection framework outperforms other approaches both in accuracy and training time for defect detection on the side wall of a glass bottle. ICIC International ©2024.

Keywords

Autoencoder, Defect detection, Machine vision