<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.</p>
</abstract>

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this...

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Data publikacji: 22 kwi 2022

Zakres stron: 107 - 111

Otrzymano: 14 gru 2021

Przyjęty: 28 lut 2022

DOI: https://doi.org/10.2478/msr-2022-0014

Słowa kluczowe
Defect inspection, metal screw, deep learning, convolutional neural network

© 2022 Wei-Yu Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Data publikacji: 22 kwi 2022

Zakres stron: 107 - 111

Otrzymano: 14 gru 2021

Przyjęty: 28 lut 2022

DOI: https://doi.org/10.2478/msr-2022-0014

Słowa kluczoweDefect inspection, metal screw, deep learning, convolutional neural network

© 2022 Wei-Yu Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Defect inspection, metal screw, deep learning, convolutional neural network