Cracked vs Non-Cracked Surface Classification

Designed a deep learning system to classify building surface images as Cracked or Non-Cracked

Applied image preprocessing and data augmentation techniques

Implemented and compared FFNN, CNN, and LSTM architectures

Analyzed model performance using accuracy and loss metrics