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Structured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector's intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process. [ABSTRACT FROM AUTHOR]

Microwave-induced thermoacoustic imaging (TAI) is a hybrid imaging technique that combines electromagnetic radiation and ultrasonic waves to achieve high imaging contrast and submillimeter spatial resolution. These characteristics make TAI a good candidate to detect material anomalies that change the material electric properties without a noticeable variation in material density. Conventional pulsed TAI systems work by sending a single short pulse to the imaged target and then detecting the generated pressure signal; therefore, a very high peak power microwave pulse or data averaging is needed to produce images with a high signal-to-noise ratio (SNR). In this paper, we propose to enhance the SNR of pulsed TAI systems by using non-coherent pulse compression. In this approach, a predefined pulse coded signal is used to illuminate the imaged sample and the received pressure signal is cross correlated with a template that is related to the power profile of the excitation signal. The proposed approach can be easily deployed to pulsed TAI systems without the need for major system modifications to the RF source because it only requires a timing circuit to control the triggering time of the RF pulses. In this paper, we demonstrate experimentally that the proposed approach highly improves the SNR of TAI signals and images and can be used to reduce the acquisition time by lowering the number of data averaging or reduce the required peak power from RF sources. [ABSTRACT FROM AUTHOR]

Laser profilometry and structured light sensors are being increasingly deployed for pipeline inspection as they provide the operator with a precise 3D map that can enable visual detection and direct insight into the integrity of the pipe. The focus of the presented paper is the design of an integrated robotic structured light sensing system used to improve the performance of 3D defect reconstruction for pipeline inspection while accommodating the uncertainty seen in a real-world environment. Point cloud registration of the consecutive 3D frames is a key factor in building this 3D map; therefore, a comprehensive featureless registration approach is proposed first, which is proven more efficient than conventional feature-based registration algorithms. Wheel odometry from the developed robotic platform and inertial measurements are integrated into the registration algorithm to enhance the 3D reconstruction performance for sensor stabilization. An intensity-based threshold searching method is further applied to retrieve the reconstructed defect size. Lastly, the uncertainties of the structured light sensing are investigated for the total reconstruction uncertainty and estimated measurement uncertainty to be quantified in order to illustrate the measurement precision. The efficacy of the proposed algorithms are supported by experimental results of pipeline inspection. • 3D profiling obtained from structured light sensors is widely applied for pipeline inspection. • Inertial Measurement Unit and Wheel odometry provide orientation and location of the robot as external input. • The proposed sensing system take the global and local positioning information from the sensor to construct a comprehensive 3D point cloud registration approach. • The proposed registration algorithm can reconstruct pipe even with no visual features in inspection, and performs better than other stats of art methods. • Uncertainty sources in this robotic integration sensing system are clarified and the uncertainty from measurement has been investigated to illustrate the reliability of the designed inspection system. [ABSTRACT FROM AUTHOR]

For safe operations and regular maintenance of pipelines in industrial and domestic usages, it is extremely important to extensively monitor gigantic pipeline systems and accurately detect harmful defects. Non-destructive evaluation (NDE) methods for inline inspection of these modern, massive pipe systems generate huge amount of data and need successful incorporation of automated defect detection algorithm that can conduct large-scale data analysis and provide instantaneous diagnosis by locating and identifying defects of varying characteristics. Here, we develop a novel NDE methodology for inspecting plastic pipelines used in natural gas distribution. Our proposed method is based on optical imaging and uses laser profiling to collect inline inspection data. In our laser profiling images, deformities due to defects lead to local perturbations in the circular rings produced by the otherwise non-defective inner walls of cylindrical pipes. Using experimental data collected by the proposed methodology, we studied the performance of a gamut of defect classification algorithms including conventional machine learning classifiers as well as deep learning architectures. Special attention was paid to take into account both the spatial and temporal features in our laser-scan image, particularly exploiting the circular shape of non-deformed laser rings. This led to the development of a hierarchical bilinear pooling (HBP) based deep learning framework that can precisely identify local perturbations in our laser-scan data by assimilating information across multiple cross-layer features. Equipping our endoscopic laser profiling system with the HBP based defect classification algorithm, we develop an integrated diagnosis method for inline data collection and automated defect characterization. Based on experimental data collected under varying profiling conditions, we demonstrate superior performance of our proposed integrated method in damage localization and classification. [ABSTRACT FROM AUTHOR]