Structural cracks have serious repercussions on the safety, adaptability, and longevity of structures. Therefore, assessing cracks is an important parameter when evaluating the quality of concrete construction. As numerous cutting-edge automated inspection systems that exploit cracks have been developed, the necessity for individual/personal onsite inspection has reduced exponentially. However, these methods need to be improved in terms of cost efficiency and accuracy. The deep-learning-based assessment approaches for structural systems have seen a significant development noticed by the structural health monitoring (SHM) community. Convolutional neural networks (CNNs) are vital in these deep learning methods. Technologies such as convolutional neural networks hold promise for precise and accurate condition evaluation. Moreover, transfer learning enables users to use CNNs without needing a comprehensive grasp of algorithms or the capability to modify pre-trained networks for particular purposes. Within the context of this study, a thorough analysis of well-known pre-trained networks for classifying the cracks in buildings made of concrete is conducted. The classification performance of convolutional neural network designs such as VGG16, VGG19, ResNet 50, MobileNet, and Xception is compared to one another with the concrete crack image dataset. It is identified that the ResNet50-based classifier provided accuracy scores of 99.91% for training and 99.88% for testing. Xception architecture delivered the least performance, with training and test accuracy of 99.64% and 98.82%, respectively.

Unmanned Aerial Vehicles (UAVs) and high-resolution images are becoming more important for Structural Health Monitoring (SHM) to observe anomalies like cracks in concrete. But cutting-edge object identification methods like YOLOv8 typically have trouble finding extremely thin cracks in large images since they have to down-sample them aggressively during standard inference. This study presents a pipeline that combines YOLOv8 with Slicing Aided Hyper Inference (SAHI) to solve the problem of detecting small objects in civil infrastructure. The study compares the usual full-image inference method with the new sliced inference method. Experimental findings demonstrate that the SAHI-integrated pipeline drastically improves the ability to find weak, fine-grained, and hairline fractures compared to traditional inference. Finally, the sliced images are merged into continuous crack structures using the morphological dilation method, thereby combining multiple overlapping bounding boxes together to form polygonal detection boxes.