Akademik Veri Yönetim Sistemi
2022 30th Signal Processing and Communications Applications Conference (SIU), Karabük, Türkiye, 15 - 18 Mayıs 2022, ss.1-4
Observing product quality is critical, cost-determining and
time-consuming process for manufacturers. Product quality tests, on the
other hand, are slow and inefficient. Human-based quality control is
highly dependent on each individual controller, and traditional
automated systems are both expensive and difficult to implement. With
the hardware and software developments in computer vision, quality
control has become fast, reliable, feasible and repeatable. In this
study, we propose a deep artificial neural network-based algorithm,
called DAfectNet, to detect defects on metal surfaces, uses visual data
detection. During synthetic data generation, DAfectNet combines various
conventional methods with a generative-adversarial network (GAN) and
yields outputs that predict the class and location. While data
generation with classical methods provided an improvement of 5.8% in
performance as average precision, this value reached 74.95% with an
increase of 8.45% using of GANs. We compared DAfectNet with the
state-of-the-art methods in addition to analyzing the effects of
transfer learning.