Single-pass inline pipeline 3D reconstruction using depth camera array

A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. In the reconstructed model of a longer pipeline, the proposed method generates the dense 3D surface reconstruction model at the millimeter level accuracy with less than 0.5% distance error. The achieved performance highlights its potential as a useful tool for efficient in-line, non-destructive evaluation of pipeline assets

A Co-optimal Coverage Path Planning Method for Aerial Scanning of Complex Structures

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm – but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to safety and inspection requirements. The result is a highly parallelizable algorithm that produces more efficient paths where quality of the useful image data is improved. The path optimization algorithm utilizes a particle swarm optimization (PSO) framework which iteratively optimizes the coverage paths without needing to discretize the motion space or simplify the sensing models as is done in similar methods. The core of the method consists of a cost function that measures both the quality and efficiency of a coverage inspection path, and a greedy heuristic for the optimization enhancement by aggressively exploring the viewpoints search spaces. To assess the proposed method, a coverage path quality evaluation method is also presented in this research, which can be utilized as the benchmark for assessing other CPP methods for structural inspection purpose. The effectiveness of the proposed method is demonstrated by comparing the quality and efficiency of the proposed approach with the state-of-art through both synthetic and real-world scenes. The experiments show that our method enables significant performance improvement in coverage inspection quality while preserving the path efficiency on different test geometries

Bridge Deck Delamination Segmentation Based on Aerial Thermography Through Regularized Grayscale Morphological Reconstruction and Gradient Statistics

Environmental and surface texture-induced temperature variation across the bridge deck is a major source of errors in delamination detection through thermography. This type of external noise poises a significant challenge for conventional quantitative methods such as global thresholding and k-means clustering. An iterative top-down approach is proposed for delamination segmentation based on grayscale morphological reconstruction. A weight-decay function was used to regularize the reconstruction for regional maxima extraction. The mean and coefficient of variation of temperature gradient estimated from delamination boundaries were used for discrimination. The proposed approach was tested on a lab experiment and an in-service bridge deck. The results demonstrated the improved capability of the framework to handle the non-uniform background, and thus eliminates the need of inferencing the background temperature which is often required by existing methods. That the results also suggested that the gradient statistics of the delamination boundary in the thermal image could be valid criterions to refine the segmentation under the proposed framework. Therefore, the authors concluded that the proposed method is a valid delamination segmentation approach for processing field concrete deck thermal images. The parameter selection and the limitation of this approach were also discussed. Further work will be carried out in more field cases to fine tune the parameter selection of the framework

Early Detection of Near-Surface Void Defects in Concrete Pavement Using Drone Based Thermography and GPR Methods

The goal of this research is to evaluate the feasibility and the performance of using UAV-mounted infrared thermography (IRT) and ground penetration radar (GPR) to detect sub-surface voids caused by consolidation issues in concrete pavement. The motivation of the study is to identify the consolidation defects as early as the initial set of concrete to avoid having this problem in large pavement sections, which is costly and time consuming to repair. Using the two technologies in combination to detect subsurface voids in the concrete initial set stage is new and aims to take advantage of the strengths and minimize the limitations of each method. UAV-based IRT can cover large areas of the pavements in a short amount of time, while GPR can provide higher accuracy in locating the defects horizontally and vertically. Therefore, the combination of the two technologies can allow detection of small voids in large areas with improved confidence. In this project, both laboratory and field tests were conducted with both methods, and coring samples were used for validation of results. The results from multiple specimens and multiple experiments suggested that both technologies performed well in detecting the subsurface voids in the concrete pavement’s initial set stage. Despite some limitations discussed in the report, the outcomes of the project provided evidence that these technologies can be used separately or together on the field as efficient and economical quality control tools in concrete pavement construction