Research on grounding grid corrosion classification method based on convolutional neural network

Du, Junping; Li-qian, Yan; Wang, Haixia; Huang, Qianrui

doi:10.1051/matecconf/201816001008

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…Atha and Jahanshahi (2018) finetuned a CNN network to classify and identify the corrosion position through the sliding window technique. Based on corrosion levels, Du et al (2018) proposed a two parallel CNN architecture to classify corrosions. Apart from aforementioned approaches, there are also some other works that adopted CNN-based object detection approaches to locate corrosions.…”

Section: Deep Learning-based Corrosion Detectorsmentioning

confidence: 99%

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Yang

Luo

et al. 2021

J Ambient Intell Human Comput

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Corrosion has been concerned as a serious safety issue for metallic facilities. Visual inspection carried out by an engineer is expensive, subjective and time-consuming. Micro Aerial Vehicles (MAVs) equipped with detection algorithms have the potential to perform safer and much more efficient visual inspection tasks than engineers. Towards corrosion detection algorithms, convolution neural networks (CNNs) have enabled the power for high accuracy metallic corrosion detection. However, these detectors are restricted by MAVs on-board capabilities. In this study, based on You Only Look Once v3-tiny (Yolov3-tiny), an accurate deep learning-based metallic corrosion detector (AMCD) is proposed for MAVs on-board metallic corrosion detection. Specifically, a backbone with depthwise separable convolution (DSConv) layers is designed to realise efficient corrosion detection. The convolutional block attention module (CBAM), three-scale object detection and focal loss are incorporated to improve the detection accuracy. Moreover, the spatial pyramid pooling (SPP) module is improved to fuse local features for further improvement of detection accuracy. A field inspection image dataset labelled with four types of corrosions (the nubby corrosion, bar corrosion, exfoliation and fastener corrosion) is utilised for training and testing the AMCD. Test results show that the AMCD achieves 84.96% mean average precision (mAP), which outperforms other state-of-the-art detectors. Meanwhile, 20.18 frames per second (FPS) is achieved leveraging NVIDIA Jetson TX2, the most popular MAVs on-board computer, and the model size is only 6.1 MB.

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Section: Deep Learning-based Corrosion Detectorsmentioning

confidence: 99%

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Yang

Luo

et al. 2021

J Ambient Intell Human Comput

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“…Atha and Jahanshahi [14] enhanced the classi cation and localization capabilities of a CNN network by implementing the sliding window technique. Du et al [15], proposed dual-parallel CNN architecture for the classi cation of corrosion levels. Apart from the aforementioned approaches, several studies have employed CNN-based object detection techniques to identify instances of corrosion.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI) Powered Detection and Identification of Pressure Vessel External Damage – YOLO-based Application

Seddik,

Hewieg,

Afify

2024

Preprint

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Regular visual inspections of pressure vessels by qualified inspectors are vital in the oil and gas industry to ensure vessel integrity, prevent catastrophic failures, and avoid the consequences of misdiagnosis. In the present paper, a deep learning (DL) model is proposed to perform visual inspection of pressure vessels using the You Only Look Once (YOLO) v8 model. Initially, binary classification is developed to diagnose whether the pressure vessel's exterior shell is in excellent condition or includes damage using a training dataset of 5000 real shell surface on-site images from the Abu Madi gas field of the PETROBEL Company, Egypt. An additional model was trained on the same dataset of binary model for multi-class identification of external damages (i.e. corrosion, painting damage, mechanical damage, or brittle fracture). Three models were compared; namely YOLO v5, YOLO v8, and YOLO v9. The highest performance model was YOLO v8. Its detection accuracy reached 93% and 91.5% for binary classification and multi-class classification models, respectively. Implementing this solution in the inspection process will result in a cost reduction since it decreases the need for scaffolding and trained inspectors. This study provides a valuable roadmap for future research on image processing-based pressure vessel damage detection.

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“…Deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), are used to detect rust on metal surfaces [13]. Du J et al [14] proposed an improved CNN model based on corrosion images to classify and evaluate the degree of corrosion in grounding grids. Yao et al [15] trained a large number of corrosion images through the CNN to obtain the classifier model, and then used the classifier model and overlapping scanning sliding window algorithm to identify and locate the corrosion of the hull structural plate.…”

Section: Introductionmentioning

confidence: 99%

A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value

Hu,

Duan,

Zhao

et al. 2023

Sensors

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Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved.

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Research on grounding grid corrosion classification method based on convolutional neural network

Cited by 7 publications

References 6 publications

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Artificial Intelligence (AI) Powered Detection and Identification of Pressure Vessel External Damage – YOLO-based Application

A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value

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