The proper inspection of a cracks pattern over time is a critical diagnosis step to provide a thorough knowledge of the health state of a structure. When monitoring cracks propagating on a planar surface, adopting a single-image-based approach is a more convenient (costly and logistically) solution compared to subjective operators-based solutions. Machine learning (ML)- based monitoring solutions offer the advantage of automation in crack detection; however, complex and time-consuming training must be carried out. This study presents a simple and automated ML-based crack monitoring approach implemented in open sources software that only requires a single image for training. The effectiveness of the approach is assessed conducting work in controlled and real case study sites. For both sites, the generated outputs are significant in terms of accuracy (~1 mm), repeatability (sub-mm) and precision (sub-pixel). The presented results highlight that the successful detection of cracks is achievable with only a straightforward ML-based training procedure conducted on only a single image of the multi-temporal sequence. Furthermore, the use of an innovative camera kit allowed exploiting automated acquisition and transmission fundamental for Internet of Things (IoTs) for structural health monitoring and to reduce user-based operations and increase safety.

Development of automated and remotely controlled procedures for accurate crack detection and analysis is an advantageous solution when compared to time-consuming and subjective crack examination conducted by operators. Recent studies have demonstrated that Machine Learning (ML) algorithms have sufficient potential for crack measurements. However, training of large amount of data is essential. When working on single sites with permanently installed fixed cameras adoption of ML solutions may be redundant. The purpose of this work is to assess the performance of a procedure for crack detection based on an easy to implement workflow supported by the use of ML and image processing algorithms. The datasets used in this work are composed of temporal sequence of single digital images. The workflow proposed includes three main modules covering acquisition, optimization and crack detection. Each module is automated and basic manual input by an operator is only required to train the classifier. The processing modules are implemented in modular open-source programs (e.g., ImageJ and Ilastik). Results obtained in controlled conditions led to a satisfactory level of detection (about 99% of the crack pattern detected). Experiments conducted on real-sites highlighted variable detection capabilities of the proposed approach (from 12 to 96%). The main limitation of the approach is the production of false-positive detection due to significant variation in illumination conditions. Further work is being conducted to define scalability of the approach and to verify deformation detection capabilities.