  Effective crack detection is pivotal for the structural health monitoring and
inspection of buildings. This task presents a formidable challenge to computer
vision techniques due to the inherently subtle nature of cracks, which often
exhibit low-level features that can be easily confounded with background
textures, foreign objects, or irregularities in construction. Furthermore, the
presence of issues like non-uniform lighting and construction irregularities
poses significant hurdles for autonomous crack detection during building
inspection and monitoring. Convolutional neural networks (CNNs) have emerged as
a promising framework for crack detection, offering high levels of accuracy and
precision. Additionally, the ability to adapt pre-trained networks through
transfer learning provides a valuable tool for users, eliminating the need for
an in-depth understanding of algorithm intricacies. Nevertheless, it is
imperative to acknowledge the limitations and considerations when deploying
CNNs, particularly in contexts where the outcomes carry immense significance,
such as crack detection in buildings. In this paper, our approach to surface
crack detection involves the utilization of various deep-learning models.
Specifically, we employ fine-tuning techniques on pre-trained deep learning
architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models
are chosen for their established performance and versatility in image analysis
tasks. We compare deep learning models using precision, recall, and F1 scores.
