Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
An Experimental Investigation into the Performance Characteristics of TiO2/Ethylene Glycol Nanofluid Flowing in a Horizontal Counterflow Heat Exchanger Read chapter Read first chapter

2022 | OriginalPaper | Chapter

58. Detection of Micro-defects on Metal Screw Surfaces Based on Faster Region-Based Convolutional Neural Network

Authors : Mohd Nor Azmi Ab Patar, Muhammad Azmi Ayub, Nur Aainaa Zainal, Muhammad Aliff Rosly, Hokyoo Lee, Akihiko Hanafusa

Published in: Intelligent Manufacturing and Energy Sustainability

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The detection of defects in a product is one of required production process for quality control. Currently, the quality control process of metal screws uses many manpower for manual inspection. Hence, this study about to implement faster region-based convolutional neural network (faster R-CNN) to detect the micro-defects on metal screw surfaces. The defects of surface damage, stripped screw, and dirty surface screw considered in this research. Raspberry Pi 3 with a camera module is used for image acquisition of the metal screws in determining various kinds of defects. The image is also acquired to be used for the training of the faster R-CNN. A testing is carried out to test the performance of the model. The experiment outcome shows that the detection accuracy of the model is 98.8%. The model also shows superiority in this project detection method compared with the traditional template-matching method and single-shot detector (SSD) model.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Analysis of Microstructural Features and Corrosion Resistance of Cryogenic Burnished and Hydroxyapatite Powder Burnished Titanium Alloy
next chapter Effects of Rotation Speeds on Electrical Submersible Pump Performance Under Two-Phase Flow
Literature
1.
Feng, X., Jiang, Y., Yang, X., Du, M., Li, X.: Computer vision algorithms and hardware implementations: a survey. Integration 69(June), 309–320 (2019)CrossRef
2.
Hassanat, B.A., Alkasassbeh, M., Al-awadi, M., Alhasanat, E.A.A.: Color-based object segmentation method using artificial neural network. Simul. Model. Pract. Theory 64, 3–17 (2015)CrossRef
3.
Kaya, E., Saritas, İ.: Towards a real-time sorting system: identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Comput. Electron. Agric., 166(July), 105016 (2019)
4.
Ali, M.H., Aizat, K., Yerkhan, K., Zhandos, T., Anuar, O.: Vision-based robot manipulator for industrial applications. Proc. Comput. Sci. 133, 205–212 (2018)CrossRef
5.
Wang, Z., Li, H., Zhang, X.: Construction waste recycling robot for nails and screws: computer vision technology and neural network approach. Autom. Constr. 97(March), 220–228 (2019)
6.
Fu, L., et al.: Kiwifruit detection in field images using faster R-CNN with ZFNet. IFAC-PapersOnLine 51(17), 45–50 (2018)CrossRef
7.
Bulnes, F.G., Usamentiaga, R., Garcia, D.F., Molleda, J.: An efficient method for defect detection during the manufacturing of web materials. J. Intell. Manuf. 27(2), 431–445 (2016)CrossRef
8.
Yapi, D., Mejri, M., Allili, M.S., Baaziz, N.: A learning-based approach for automatic defect detection in textile images. IFAC-PapersOnLine 28(3), 2423–2428 (2015)CrossRef
9.
Ferguson, M., Ak, R., Lee, Y.-T.T., Law, K.H.: Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain. Manuf. Syst. 2(1), 20180033 (2018)CrossRef
10.
Chen, J., Liu, Z., Wang, H., Nunez, A., Han, Z.: Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans. Instrum. Meas. 67(2), 257–269 (2018)CrossRef
11.
Song, L., Li, X., Yang, Y., Zhu, X., Guo, Q., Yang, H.: Detection of micro-defects on metal screw surfaces based on deep convolutional neural networks. Sensors (Switzerland) 18(11) (2018)
12.
Sun, X., Gu, J., Huang, R., Zou, R., Palomares, B.G.: Surface defects recognition of wheel hub based on improved faster R-CNN. Electron. 8(5), 1–16 (2019)
13.
Zhou, Z., Lu, Q., Wang, Z., Huang, H.: Detection of micro-defects on irregular reflective surfaces based on improved faster r-cnn. Sensors (Switzerland) 19(22), 1–15 (2019)
14.
da Costa, Z., Figueroa, H.E.H., Fracarolli, J.A.: Computer vision based detection of external defects on tomatoes using deep learning. Biosyst. Eng. 190, 131–144 (2020)CrossRef
15.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRef
16.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2016, December 2016, pp. 2818–2826 (2016)
17.
Ayub, M.A., Ab Patar, M.N.A., Zainal, N.A.: ‘AZ’ humanoid robot head with object and color tracking capabilities. Int. J. Mech. Eng. Robot. Res. 9(6) (2020)
18.
Vidhyapeeth Rajasthan Neelam Sharma, B., Banasthali Vidhyapeeth Rajasthan, A.: An overview of various template matching methodologies in image processing. Int. J. Comput. Appl. 153(10), 975–8887 (2016)
19.
Huang, J. et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017, January 2017, pp. 3296–3305 (2017)
Metadata
Title
Detection of Micro-defects on Metal Screw Surfaces Based on Faster Region-Based Convolutional Neural Network
Authors
Mohd Nor Azmi Ab Patar
Muhammad Azmi Ayub
Nur Aainaa Zainal
Muhammad Aliff Rosly
Hokyoo Lee
Akihiko Hanafusa
Copyright Year
2022
Publisher
Springer Singapore
Book
Intelligent Manufacturing and Energy Sustainability

Print ISBN: 978-981-16-6481-6

Electronic ISBN: 978-981-16-6482-3

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-6482-3_58

Premium Partners

    Image Credits