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Detection of Material Surface Damage Based on a Photograph
Marek, Radek ; Sakin, Martin (referee) ; Dyk, Tomáš (advisor)
This work focuses on the use of various types of neural networks for detecting surface damage of materials from photographs and evaluates their effectiveness. Identifying different types of damage, such as cracks, scratches, and other defects, is essential for assessing the condition of materials and may indicate the need for further maintenance or repairs. The use of advanced neural networks allows for more precise detection and classification of damage, which is crucial for applications in areas such as construction, the automotive industry, and aerospace engineering, where rapid and reliable diagnostics of material defects are critical. Integrating these technologies into regular inspection processes can significantly improve accident prevention and extend the lifespan of structural components. The work also discusses the possibilities for improvement and adaptation of algorithms to specific materials and types of damage. Thus, this work demonstrates how advanced machine learning technologies can significantly contribute to more effective and reliable material condition monitoring, opening paths for future innovations in maintenance and safety.
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