Deep learning (DL) is proposed as a solution to enhance structural defect detection using digital images. Traditional image processing algorithms (IPAs) are limited in their ability to adapt to uncontrolled environments and require manual feature extraction, which is time-consuming and error-prone. DL models, particularly convolutional neural networks (CNNs), offer a more efficient and accurate approach by automatically learning features from raw data without manual intervention. This thesis explores the application of DL in structural health monitoring (SHM), focusing on crack detection and segmentation. The research aims to develop DL-based methods for detecting structural defects in uncontrolled environments, such as outdoor settings. The study introduces a DL model for classifying images into crack or non-crack categories, which demonstrates superior performance compared to traditional IPAs. Additionally, a segmentation model is developed to provide more accurate and intuitive results for identifying cracks in images. The segmentation model outperforms existing methods in terms of speed, robustness, and flexibility. The thesis also addresses the challenges of training DL models, including overfitting, vanishing gradients, and the need for large datasets. Techniques such as data augmentation, normalization, and regularization are employed to improve model performance. The study highlights the importance of using large, diverse datasets for training DL models, as well as the need for adaptive optimization strategies to ensure effective learning. The research contributes to the field of SHM by demonstrating the potential of DL in automating structural defect detection. The proposed methods are validated through extensive experiments, showing that DL models can effectively detect and segment cracks in images, even under varying environmental conditions. The results indicate that DL-based approaches are more reliable and efficient than traditional IPAs, making them a promising solution for future SHM systems. The study also emphasizes the importance of continued research in developing robust and scalable DL models for structural health monitoring.Deep learning (DL) is proposed as a solution to enhance structural defect detection using digital images. Traditional image processing algorithms (IPAs) are limited in their ability to adapt to uncontrolled environments and require manual feature extraction, which is time-consuming and error-prone. DL models, particularly convolutional neural networks (CNNs), offer a more efficient and accurate approach by automatically learning features from raw data without manual intervention. This thesis explores the application of DL in structural health monitoring (SHM), focusing on crack detection and segmentation. The research aims to develop DL-based methods for detecting structural defects in uncontrolled environments, such as outdoor settings. The study introduces a DL model for classifying images into crack or non-crack categories, which demonstrates superior performance compared to traditional IPAs. Additionally, a segmentation model is developed to provide more accurate and intuitive results for identifying cracks in images. The segmentation model outperforms existing methods in terms of speed, robustness, and flexibility. The thesis also addresses the challenges of training DL models, including overfitting, vanishing gradients, and the need for large datasets. Techniques such as data augmentation, normalization, and regularization are employed to improve model performance. The study highlights the importance of using large, diverse datasets for training DL models, as well as the need for adaptive optimization strategies to ensure effective learning. The research contributes to the field of SHM by demonstrating the potential of DL in automating structural defect detection. The proposed methods are validated through extensive experiments, showing that DL models can effectively detect and segment cracks in images, even under varying environmental conditions. The results indicate that DL-based approaches are more reliable and efficient than traditional IPAs, making them a promising solution for future SHM systems. The study also emphasizes the importance of continued research in developing robust and scalable DL models for structural health monitoring.