Vision Transformer based knowledge distillation for fasteners defect detection

Sampath, V.; Maurtua, Iñaki; Martín, Juan José Aguilar; Iriondo, Ander; Lluvia, Iker; Pinto, Andoni Rivera

doi:10.1109/icecet55527.2022.9872566

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the places of leakage fields, the particles are attracted and clustered. The defects can provide visible indications under ultraviolet light [ 2 ]. There are several factors that influence the effectiveness of MPI.…”

Section: Introductionmentioning

confidence: 99%

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

Sampath

Maurtua

Martín

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Surface defect identification based on computer vision algorithms often leads to inadequate generalization ability due to large intraclass variation. Diversity in lighting conditions, noise components, defect size, shape, and position make the problem challenging. To solve the problem, this paper develops a pixel-level image augmentation method that is based on image-to-image translation with generative adversarial neural networks (GANs) conditioned on fine-grained labels. The GAN model proposed in this work, referred to as Magna-Defect-GAN, is capable of taking control of the image generation process and producing image samples that are highly realistic in terms of variations. Firstly, the surface defect dataset based on the magnetic particle inspection (MPI) method is acquired in a controlled environment. Then, the Magna-Defect-GAN model is trained, and new synthetic image samples with large intraclass variations are generated. These synthetic image samples artificially inflate the training dataset size in terms of intraclass diversity. Finally, the enlarged dataset is used to train a defect identification model. Experimental results demonstrate that the Magna-Defect-GAN model can generate realistic and high-resolution surface defect images up to the resolution of 512 × 512 in a controlled manner. We also show that this augmentation method can boost accuracy and be easily adapted to any other surface defect identification models.

show abstract

Section: Introductionmentioning

confidence: 99%