Deep learning-based concrete image analysis and generalization capability - Qian, Hanjie (Nanyang Technological University, 2024)
Concrete is one of the most widely used materials in the world, playing a fundamental role in modern human civilization. The analysis of concrete's microstructure contributes to the design and improvement of concrete compositions. Scanning electron microscopy (SEM) imaging is the most common and direct method for analyzing the microstructure of concrete. However, previous research on SEM image analysis has encountered several challenges: low accuracy and efficiency, lack of quantitative research, poor generalization capability, and dependency on labels. The aim of this thesis is to develop a highly automated and accurate method for analyzing concrete SEM images while improving generalization capability through transfer learning. To achieve this, we propose analysis algorithms based on deep learning and design methods to enhance generalization capability and alleviate the reliance on labels.