Deep Learning Activation Layer-Based Wall Quality Recognition Using Conv2D ResNet Exponential Transfer Learning Model

• Published in: Mathematics (Basel) Vol. 10; no. 23; p. 4602
• Main Authors: Kim, Bubryur, Natarajan, Yuvaraj, Munisamy, Shyamala Devi, Rajendran, Aruna, Sri Preethaa, K. R., Lee, Dong-Eun, Wadhwa, Gitanjali
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Accuracy; activation layer; Algorithms; Analysis; Artificial neural networks; Automation; Classification; Concrete; Conv2D; Cracks; Damage detection; Datasets; Deep learning; Efflorescence; F-score; Food science; Functions, Exponential; Inspection; Machine learning; Methods; Model accuracy; Neural networks; Optimization techniques; Parameter estimation; Quality management; Structural safety; Support vector machines; transfer learning; Unmanned aerial vehicles; Walls; South Korea

Crack detection is essential for observing structural health and guaranteeing structural safety. The manual crack and other damage detection process is time-consuming and subject to surveyors’ biased judgments. The proposed Conv2D ResNet Exponential model for wall quality detection was trained with...