Classification of damage types in liquid-filled buried pipes based on deep learning

ma, qi; Du, Guanglong; Yu, Zeyu; Yuan, Hongqiang; Wei, Xiaolong

doi:10.1088/1361-6501/ac9b7b

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…To further demonstrate the effectiveness of the algorithm proposed in this paper, the improved YOLOv7 algorithm is compared with YOLOv7+CBAM+SPPFCSPC [41], YOLOv5+CA [42], YOLOv5+BiFPN [43], YOLOv4+ Densenet [44], Faster-rcnn+Resnet [45], and Centernet [46] in this paper. The average accuracy of each algorithm and the recognition speed are shown in Table 4.…”

Section: Results and Comparisonmentioning

confidence: 99%

Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

Liu

Zhang

et al. 2023

IEEE Access

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As the grid coverage rises, foreign objects invade more and more frequently, causing grid failures to rise every year. To address this issue, this paper proposes a deep learning-based transmission line unmanned inspection of foreign objects recognition algorithm. The algorithm is based on YOLOv7 (You Only Look Once) algorithm, combining with hyperparameter optimization based on genetic algorithm (GA) and space-to-depth (SPD) convolution to complete the foreign object recognition of transmission line Unmanned Aerial Vehicle (UAV) images. The proposed method can promptly determine and locate these targets' presence in aerial images. Finally, this paper compares the improved YOLOv7 algorithm with other YOLO series algorithms (Faster-rcnn, Centernet, and other target detection models). The comparison results show that the method has the highest Mean Average Precision (mAP) of 92.2% and the Frames Per Second (FPS) of 19 is second only to Centernet. Compared with the unimproved YOLOv7, the average accuracy in the recognition of tower cranes has increased by 11.9%, which is the most obvious improvement in accuracy compared with other detection targets. Meanwhile, the hyperparameter optimization based on genetic algorithm speeds up the convergence of the model.

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Section: Results and Comparisonmentioning

confidence: 99%

Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

Liu

Zhang

et al. 2023

IEEE Access

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“…One magnetostrictive patch transducer based on guided wave was proposed to identify the axial location of pipeline defects [6]. Deep residual neural network combined with guided wave was used to implement defect classification [7]. Research has also been conducted on the detection of special shaped pipelines, such as semi-exposed pipe [8], elbow [9], pipe embedded in concrete [10].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Lv,

Chen,

Tong

et al. 2023

Meas. Sci. Technol.

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Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

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“…Ultrasonic guided waves (UGWs) have gained substantial attention in nondestructive testing (NDT) [1,2] and structural health monitoring (SHM) [3,4] for the low attenuation and high propagation velocity, which are widely adopted in the detection of pipes [5], strands [6], laminates [7], rails [8], bones [9], etc. Notably, considering the ability of UGW testing to characterize the pipe defects that may affect current and future performance, it has also been applied to fluid-filled pipes [10], underground pipes [11], underwater pipes [12], coated pipes [13] and other pipeline systems that take into account the operating situation.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Water-Filled Pipe Ultrasonic Guided Wave Signals in the Distance Domain by Orthogonal Matching Pursuit Based on Dispersion and Multi-Mode

Wang,

Tang,

Gong

et al. 2023

Sensors

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Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%.

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Classification of damage types in liquid-filled buried pipes based on deep learning

Cited by 8 publications

References 26 publications

Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Reconstruction of Water-Filled Pipe Ultrasonic Guided Wave Signals in the Distance Domain by Orthogonal Matching Pursuit Based on Dispersion and Multi-Mode

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