Defect Detection From Ultrasound Images

Problem koji ovaj rad opisuje i pokušava riješiti zasniva se na klasifikacijama slika s nepravilnostima i bez, uz uvjet da se na ispitni materijal/predmet ne nanosi šteta(NDT metode). NDT metode su obližnje opisane u uvodu zajedno s primjerima rješavanja sličnih problema korištenjem starijih metoda strojnog učenja. Kako se pristup rješavanja problema u ovom radu zasniva na dubokom učenju, objašnjene su osnove neuronskih mreža, dubokog učenja te konvolucijskih neuronskih mreža. U nastavku su objašnjene osnove dva široko korištena modela konvolucijskih neuronskih mreža primijenjenih u ovom radu, kao i jedan proizvoljno napravljeni model za usporedbu performansi. Objašnjena je programska izvedba na Google colabu kao i popratne tehnologije a kako je ispitni skup slika veoma bitan dio rada njima je također posvećeno poglavlje za detaljniji opis. Prije prikazivanja eksperimentalnih rezultata ukratko su opisane metrike uspješnosti te razlog korištenja odabranih. Rezultati su prikazani u obliku grafova i tablice nadopunjeni popratnim komentarima i bitnim dijelovima o procesu učenja modela. Na kraju je sve objedinjeno u zadnjem poglavlju zaključka i diskusije gdje razmatramo uspješnost rada, njegova ograničenja kao i moguće daljne korake.The problem that this paper describes and tries to solve is based on the classification of pictures with and without irregularities, provided that no damage is caused to the test material / object (NDT methods). NDT methods are described in more detail in the introduction along with examples of solving similar problems using older machine learning methods. Since the approach of problem solving in this paper based on deep learning, the basics of neural networks, deep learning and convolutional neural networks are explained. The basics of two widely used models of convolutional neural networks applied in this paper are explained below, as well as one arbitrarily constructed model for performance comparison. The program implementation on the Google Colab as well as the accompanying technologies are explained, and as the test set of images is a very important part of the work, a chapter for a detailed description is also dedicated to them. Before presenting the experimental results, metric performance was briefly described as well as the reason for using the selected ones. The results are presented in the form of graphs and tables supplemented by accompanying comments and essential parts of model learning process. Finally, everything is summarized in the last chapter, conclusion and discussion, where the success of the work, its limitations as well as possible further steps are discussed