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Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

Aiming at the sparse problem of object point cloud based on structure from motion method, a 3D reconstruction method using different matching data is proposed. The matching points are calculated by contrast context histogram(CCH) algorithm. The M-estimation sampling consensus(MSAC) algorithm is used to calculate the fundamental matrix, the translation and rotation matrix are decomposed from fundamental matrix. The image projection matrix is obtained combining the camera internal parameters. KLT algorithm is used to update the matching data, and the point cloud is generated by triangulation principle. This method makes use of the advantage of high accuracy of CCH algorithm to make the calculation results of the basic matrix converge. Using KLT algorithm to realize the matching by displacement instead of description vector, it makes up for the deficiency of matching data in low frequency region. The experimental results show that the proposed algorithm is effective and feasible, and the reconstructed point cloud has advantages in comparison with existing algorithms, it can be used for building 3D model of objects in the real scene.