Intelligent identification of metal corrosion based on Corrosion-YOLOv5s

Jia, Zhitong; Fu, Mingjiao; Zhao, Xiaodong; Cui, Zhendong

doi:10.1016/j.displa.2022.102367

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…The YOLOv5s network structure [22] is divided into four parts: input, backbone, feature fusion network and output [23]. At the input end, mosaic data enhancement [24] is adopted to splice the input images by random scaling, cropping, arrangement, etc, to enrich the dataset and improve the robustness of the network.…”

Section: The Main Structure Of the Yolov5 Algorithmmentioning

confidence: 99%

A global centralized magnetic flux leakage small defect detection network

Chen,

Lang,

Liu

2024

Eng. Res. Express

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To solve the problem that magnetic-flux-leakage (MFL) small defects are difficult to accurately detect by machine learning methods, a global centralized magnetic flux leakage small defect detection network (RCFPNet) is proposed. RCFPNet consists of simulation data enhancement, improved feature extraction (backbone), an improved centralized feature pyramid (CFP) and a detection head network. The MFL defect data of various scales and shapes are simulated by ANSYS simulation software and superimposed with the actual detected MFL defects to expand the dataset. The Repvgg module is used to replace the 3*3 convolution of the backbone to improve the detection speed. An improved spatially explicit vision center scheme (EVC) and a global centralized regulation rule (GCR) for feature fusion networks are proposed for feature fusion networks. RCFPNet is based on an improvement of the YOLOv5 network. Experiments have proven that RCFPNet has improved detection speed and accuracy and has achieved good results in the detection of magnetic leakage small defects. Experiments show that when the IOU=0.5, the accuracy rate of this algorithm is 96.1%, and the reasoning time is 8.9 ms.

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Section: The Main Structure Of the Yolov5 Algorithmmentioning

confidence: 99%

A global centralized magnetic flux leakage small defect detection network

Chen,

Lang,

Liu

2024

Eng. Res. Express

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“…Matthaiou et al [ 25 ] achieved good results by training SSD using transfer learning technology to detect corrosion objects. Jia et al [ 26 ] proposed the Corrosion-YOLOv5s metal corrosion target detection model based on the YOLOv5s model, which achieved an accuracy rate of 90.5%.…”

Section: Related Workmentioning

confidence: 99%

Detection and recognition of metal surface corrosion based on CBG-YOLOv5s

Fu,

Jia,

et al. 2024

PLoS ONE

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The automatic detection of the degree of surface corrosion on metal structures is of significant importance for assessing structural damage and safety. To effectively identify the corrosion status on the surface of coastal metal facilities, this study proposed a CBG-YOLOv5s model for metal surface corrosion detection, based on the YOLOv5s model. Firstly, we integrated the Convolutional Block Attention Module (CBAM) into the C3 module and developed the C3CBAM module. This module effectively enhanced the channel and spatial attention capabilities of the feature map, thereby improving the feature representation. Second, we introduced a multi-scale feature fusion concept in the feature fusion part of the model and added a small target detection layer to improve small target detection. Finally, we designed a lighter C3Ghost module, which reduced the number of parameters and the computational load of the model, thereby improving the running speed of the model. In addition, to verify the effectiveness of our method, we constructed a dataset containing 6000 typical images of metal surface corrosion and conducted extensive experiments on this dataset. The results showed that compared to the YOLOv5s model and several other commonly used object detection models, our method achieved superior performance in terms of detection accuracy and speed.

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“…Their improved model demonstrated the capability to effectively and accurately perform real-time target identi cation tasks on embedded devices with minimal power consumption. Jia et al [22] established a model framework for detecting metal surface corrosion. Their study resulted in the development of the corrosion-YOLO v5s model; a framework for identifying metal surface corrosion based on YOLO v5s model.…”

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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Intelligent identification of metal corrosion based on Corrosion-YOLOv5s

Cited by 15 publications

References 27 publications

A global centralized magnetic flux leakage small defect detection network

A global centralized magnetic flux leakage small defect detection network

Detection and recognition of metal surface corrosion based on CBG-YOLOv5s

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

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