Structure from Motion 3D reconstruction has become widely used in recent years in a number of fields such as industrial surface inspection, archeology, cultural heritage preservation and geomapping. A number of software solutions have been released using variations of this technique. In this paper we analyse the state of the art of these software applications, by comparing the resultant 3D meshes qualitatively and quantitatively. We propose a number of testing scenarios using different lighting conditions, camera positions and image acquisition methods for the best in-depth analysis and discuss the results, the overall performance and the problems present in each software. We employ distance and roughness metrics for evaluating the final reconstruction results.

Over time, erosion of the leading edge of wind turbine blades increases the leading-edge roughness (LER). This may reduce the aerodynamic performance of the blade and hence the annual energy production of the wind turbine. As early detection is key for cost-effective maintenance, inspection methods are needed to quantify the LER of the blade. The aim of this proof-of-principle study is to determine whether high-resolution Structure-from-Motion (SfM) has the sufficient resolution and accuracy for quantitative inspection of LER. SfM provides 3D reconstruction of an object geometry using overlapping images of the object acquired with an RGB camera. Using information of the camera positions and orientations, absolute scale of the reconstruction can be achieved. Combined with a UAV platform, SfM has the potential for remote blade inspections with a reduced downtime. The tip of a decommissioned blade with an artificially enhanced erosion was used for the measurements. For validation, replica molding was used to transfer areas-of-interest to the lab for reference measurements using confocal microscopy. The SfM reconstruction resulted in a spatial resolution of 1 mm as well as a sub-mm accuracy in both the RMS surface roughness and the size of topographic features. In conclusion, high-resolution SfM demonstrated a successful quantitative reconstruction of LER.

One of the time consuming tasks in the timber industry is the manually measurement of features of wood stacks. Such features include, but are not limited to, the number of the logs in a stack, their diameters distribution, and their volumes. Computer vision techniques have recently been used for solving this real-world industrial application. Such techniques are facing many challenges as the task is usually performed in outdoor, uncontrolled, environments. Furthermore, the logs can vary in texture and they can be occluded by different obstacles. These all make the segmentation of the wood logs a difficult task. Graph-cut has shown to be good enough for such a segmentation. However, it is hard to find proper graph weights. This is exactly the contribution of this paper to propose a method for setting the weights of the graph. To do so, we use Circular Hough Transform (CHT) for obtaining information about the foreand background regions of a stack image, and then use this together with a Local Circularity Measure (LCM) to modify the weights of the graph to segment the wood logs from the rest of the image. We further improve the segmentation by separating overlapping logs. These segmented wood logs are finally scaled and used to acquire the necessary wood stack measurements in real-world scale (in cm). The proposed system, which works automatically, has been tested on two different datasets, containing real outdoor images of logs which vary in shapes and sizes. The experimental results show that the proposed approach not only achieves the same results as the state-of-the-art systems, it produces more stable results.

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The wind energy sector faces a constant need for annual inspections of wind turbine blades for damage, erosion and cracks. These inspections are an important part of the wind turbine life cycle and can be very costly and hazardous to specialists. This has led to the use of automated drone inspections and the need for accurate, robust and inexpensive systems for localization of drones relative to the wing. Due to the lack of visual and geometrical features on the wind turbine blade, conventional SLAM algorithms have a limited use. We propose a cost-effective, easy to implement and extend system for on-site outdoor localization and mapping in low feature environment using the inexpensive RPLIDAR and an 9-DOF IMU. Our algorithm geometrically simplifies the wind turbine blade 2D cross-section to an elliptical model and uses it for distance and shape correction. We show that the proposed algorithm gives localization error between 1 and 20 cm depending on the position of the LiDAR compared to the blade and a maximum mapping error of 4 cm at distances between 1.5 and 3 meters from the blade. These results are satisfactory for positioning and capturing the overall shape of the blade.