Ultrasound Image Analysis Using Deep Learning for Nondestructive Testing

U nuklearnoj industriji vrlo je bitno u što kraćem roku naći eventualne defekte u materijalu, kako bi nuklearna elektrana što prije nastavila s radom. Takod̄er, izuzetno je bitno koristiti metode nedestruktivnog ispitivanja, s obzirom na to da su dijelovi jako skupi, a jedna od najpopularnijih je ultrazvučno ispitivanje. Te defekte traže ljudi, no med̄utim vrlo se rijetko neki nad̄u, a sam proces je vrlo mukotrpan. Stoga se koriste duboke neuronske mreže, kako bi se negativni primjeri što prije eliminirali. C-snimci su vrlo korisni za analizu, s obzirom na to da otkrivaju „3. dimenziju“ defekata, te se stoga ovaj Rad bavi automatskom detekcijom defekata korištenjem RetinaNet modela s ResNet50 backbone-om. Dobiveni rezultati imaju prostora za napredak, vjerojatno zbog prevelike kompleksnosti RetinaNet modela, te je potrebno daljnje istraživanje u ovom području.In the nuclear industry it’s very important to find eventual defects in the material as soon as possible, to keep the halting of the nuclear power plant to the minimum. In addition to that, nondestructive evaluation methods are critical, due to the price of the parts used, and one of the most popular ones is ultrasonic testing. These defects are human-searched, but they are very rarely found, and the process is very exhausting. That’s why deep neural networks are being used, to eliminate the negative examples quickly. C-scans are very useful for analysis, due to the fact that they reveal "the 3. dimension" of defects, and that’s why this Thesis is doing automatic detection using the RetinaNet model with ResNet50 backbone. The end results have space for improvements, probably because the RetinaNet model is too complex, and therefore further research is necessary