Surface defect detection with limited data based on SSD detector and Siamese networks
https://doi.org/10.17586/2226-1494-2024-24-6-972-981
Abstract
This study presents an algorithm for the problem of detecting defects on hard surfaces when trained with zero or a small number of examples, addressing the challenge of limited data availability. The existing defect detection methodology using machine vision is enhanced. A hybrid approach is proposed, combining the strengths of the SSD detector and Siamese Neural Networks (SNN). The SSD detector extracts feature vector representations from images, while the SNNs are used to construct the feature space. The new approach demonstrates high accuracy in detecting both known and previously unseen defects in the training dataset. Based on testing across seven different datasets, the model showed good performance in scenarios with a limited number of training examples. A comparative analysis with existing models highlights the high performance of the proposed approach and its potential as an innovative and effective solution for the universal detection of defects on hard surfaces.
About the Authors
N. P. Novgorodcev
ITMO University
Russian Federation
Russian Federation
Nikita P. Novgorodcev - Student,
Saint Petersburg, 197101
K. A. Baturina
ITMO University
Russian Federation
Russian Federation
Kseniia A. Baturina - Student, Laboratory Assistant,
Saint Petersburg, 197101
V. A. Efimova
ITMO University
Russian Federation
Russian Federation
Valeria A. Efimova - PhD, Assistant,
Saint Petersburg, 197101
A. A. Shalyto
ITMO University
Russian Federation
Russian Federation
Anatoly A. Shalyto - D.Sc., Full Professor, Chief Researcher,
Saint Petersburg, 197101
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Review
For citations:
Novgorodcev N.P., Baturina K.A., Efimova V.A., Shalyto A.A. Surface defect detection with limited data based on SSD detector and Siamese networks. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2024;24(6):972-981. https://doi.org/10.17586/2226-1494-2024-24-6-972-981
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