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Effective crack detection is pivotal for the structural health monitoring andinspection of buildings. This task presents a formidable challenge to computervision techniques due to the inherently subtle nature of cracks, which oftenexhibit low-level features that can be easily confounded with backgroundtextures, foreign objects, or irregularities in construction. Furthermore, thepresence of issues like non-uniform lighting and construction irregularitiesposes significant hurdles for autonomous crack detection during buildinginspection and monitoring. Convolutional neural networks (CNNs) have emerged asa promising framework for crack detection, offering high levels of accuracy andprecision. Additionally, the ability to adapt pre-trained networks throughtransfer learning provides a valuable tool for users, eliminating the need foran in-depth understanding of algorithm intricacies. Nevertheless, it isimperative to acknowledge the limitations and considerations when deployingCNNs, particularly in contexts where the outcomes carry immense significance,such as crack detection in buildings. In this paper, our approach to surfacecrack detection involves the utilization of various deep-learning models.Specifically, we employ fine-tuning techniques on pre-trained deep learningarchitectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These modelsare chosen for their established performance and versatility in image analysistasks. We compare deep learning models using precision, recall, and F1 scores.

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models