Abstract. The oil and gas offshore industry demands regular inspections of components and structures that are subjected to extreme operational and environmental conditions. In this context, risers are pipelines that transport mainly oil, gas, water, and cables between submarine structures and the surface offshore platform, in the portion not touching the ocean floor. The emerged part of these risers is typically inspected by industrial climbing, which is a very time-consuming activity, has high operational costs, is dangerous and has a strong dependence on inspector skills. Remotely Piloted Aircraft Systems (RPAS) have been recently used for visual inspection of risers, however, no quantitative or geometrical evaluation has been conducted using this kind of image acquisition yet. An image-based measurement technique, such as close-range photogrammetry, can provide a 3D reconstruction using images, but a series of requisites is mandatory to achieve good results as image acquisition sequence, overlap, camera positioning network, spatial resolution and object texture in non-prepared and targetless scenes. The analysis of different image acquisition strategies using a real RPAS is too difficult because it demands a lot of time, good weather, daylight, and a scene similar to where risers are installed. An alternative is to use simulation. In this paper a ROS/Gazebo simulation is described and used to create a realistic textured 3D virtual environment of the platform, risers and RPA, providing a fast and low-cost solution to simulate different RPA trajectories for photogrammetry image acquisition in targetless scenes. These trajectories are evaluated by comparing the measured risers through photogrammetry to its CAD/simulated model. Since the scene is not prepared, the RPA position/orientation or a stereo vision setup can be used to set scale to the measurement result. The best trajectory found during simulations was also evaluated in a real experiment.

Abstract. The purpose of this paper is to show how deep learning techniques, based on CNNs, can contribute to photogrammetry process to perform geometric inspections of risers on offshore platforms. The photogrammetry process has a problematic related to the relative movements presented in the scene where the images are being acquired (dynamic photogrammetry). As an alternative solution, this work proposes the use of the YOLOv2 architecture, because this detector complies with some requirements of speed and good performance considering the functional requisites of the study executed. Thus, the purpose of this model is to detect risers and i-tubes on offshore platforms, then extract an inspection riser from the scene. Finally, with the images obtained, a 3D reconstruction is performed, followed by the results’ analyses.

Abstract. In oil and gas offshore platforms, special pipelines as flexible risers make the connection between the ocean floor structures and the platform in extreme environmental and operational conditions. Periodic inspections are necessary to assess their integrity. As industrial climbing for inspection is expensive, extremely dangerous and time consuming, qualitative visual inspection with Remotely Piloted Aircraft System (RPAS), also known as drones, are being successfully applied for remote inspection of offshore flares and risers in a much safer, quicker, and cheaper way. These experiences motivate the 3D photogrammetric inspection of risers using RPAS, considering restrictions like layout of the inspected structures and surroundings and inability to prepare the scene. In this paper, taking advantage of the position information provided by the RPAS, the reconstruction and scale of the test scene were made using only GNSS data, GNSS and scale bars, RTK, and RTK and scale bars. Calibrated artifacts were used to evaluate the results and they include a PVC pipe with artificial defects simulating a riser, a pyramidal pattern with four spheres, and scale bars. The results showed that, as expected, the worst results are for GNSS data with error standard deviations of 0.35 mm compared with 0.20 mm or less for other options. For the sphere’s artifact, relative maximum sphere spacing errors are 9.3% for GNSS, 1.9% for RTK and 0.26% using scale bars. In any case it was possible to identify the defects in the pipe with good quality and with much more detail compared with a climbing inspection.

Abstract. This work describes a control solution for real time object tracking in images acquired for a RPAS on an object inspection environment. This, controlling a 3-axis gimbal mechanism to control a camera orientation embedded to a RPAS, using its image processed for feedback. The objective of control is to maintain the target of interest at the center of the image plane. The proposed solution uses a YOLOv3 object detection model in order to detect the target object and determine, thru rotation matrices, the new desired angles to converge the object’s position to the center of the image. To compare results of the proposed control, a linear control was tuned using a linear PI algorithm. Simulation and practice experiments successfully tracked the desired object in real time using YOLOv3 in both control approaches presented.