An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method

This study introduces a pioneering system for real-time classification and measurement of concrete surface cracks, a crucial aspect of Structural Health Monitoring (SHM). We harness the power of transfer learning (TL) in Convolutional Neural Networks (CNNs), including renowned models such as MobileNetV2, EfficientNetV2, InceptionV3, and ResNet50. Notably, our model excels, particularly with TL MobileNetV2, achieving remarkable results – a 99.87% accuracy rate, 99.74% recall, 100% precision, and an impressive 99.87% F1-score. Incorporating the Otsu method for image segmentation, our system accurately assesses individual crack sizes. To refine measurements, Euclidean distance calculations and a 'pixel per inch' technique, accounting for video resolution, ensure millimeter-level width estimations. Precision is validated through manual experiments using a vernier caliper, specifically the Mitutoyo Absolute Digital Caliper. This tool ensures high accuracy, with an error margin of ±0.2 mm to ±0.3 mm, making it efficient for detailed measurements. Despite these promising outcomes, it is crucial to acknowledge inherent limitations. These include dependence on image quality, challenges in generalization, sensitivity to training data, assumption of linear crack width calculation, resolution dependency, and other factors. These limitations underscore the need for further refinement in our proposed classification model and measurement technique. This research represents a significant advancement in the SHM field, catering to early detection and timely maintenance requirements essential for infrastructure safety and longevity. As the field increasingly prioritizes rapid detection, our model presents a versatile solution that enhances the potential of Structural Health Monitoring.