| Title |
Corrosion detection on steel panels using semantic segmentation models / |
| Translation of Title |
Korozijos aptikimas ant plieninių plokščių naudojant semantinės segmentacijos modelius. |
| Authors |
Micikevičius, Mantas |
| Full Text |
|
| Pages |
34 |
| Keywords [eng] |
Corrosion detection, Semantic Segmentation, Convolutional Neural Networks, Image prepossessing algorithm, Images masking. Korozijos aptikimas, semantinė segmentacija, konvoliuciniai neuroniniai tinklai, paveikslėlių apdorojimo algoritmas, paveikslėlių maskavimas. |
| Abstract [eng] |
The effectiveness of several semantic segmentation networks, including U-Net, FPN, PSPNet, and LinkNet, is examined in this study for the purpose of corrosion detec- tion on various steel panels that have been affected by various chemicals that cause corrosion to emerge. Since these types of images lack publically accessible ground truth datasets, an image prepossessing algorithm was developed and is now employed both fully automatically and semi-manually. Both approaches are compared in or- der to determine the significance of image masking accuracy and human involvement throughout the process. The aim is to examine the performance of neural networks by comparing their performance metrics not only between the models used in this study, but also with those from earlier studies that used fully automated corrosion detection algorithms or various deep learning architectures. The findings indicate that manually creating ground truth datasets has a significant impact on model accuracy metrics, and that only models trained with these types of datasets may be employed successfully in real-world applications. Additionally, compared to various corrosion detection techniques used in other reviewed researches, all architectures tested in this thesis worked well and demonstrated superior results in majority of the used indicators. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2023 |
