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automatic defect detection of pipelines based on improved OFG-YOLO algorithm

MEASUREMENT 2025年 242卷

作者： Yang, Denglian Ma, Chongsen Yu, Guangtao Chen, Yun Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Hunan Peoples R China Changsha Univ Sci & Technol Sch Traff & Transportat Engn Changsha 410114 Hunan Peoples R China

Underground metal pipelines are a key mode of long-distance transportation for oil, gas, and water owing to their low transport costs and high safety standards. Nevertheless, these pipelines are vulnerable to defects that gradually emerge during operation, potentially impairing their functionality and maintenance. The timely detection and maintenance of defects in metal pipelines is therefore of great significance. Although pipeline defects are presently detected via closed-circuit television (CCTV) and ultrasonic time-of-flight diffusion technology (TOFD), these methods depend upon engineering experience and are not sufficiently intuitive. In contrast, the application of image recognition for pipeline defect detection has demonstrated considerable potential. This paper presents an automatic metal pipeline defect detection method based on the YOLO architecture, which was modified by enhancing the convolutional, feature fusion, and regression layers, as well as the spatial pyramid pooling module. Our experimental results demonstrate that the enhanced model outperforms mainstream algorithms in terms of various evaluation indicators.

关键词： automatic defect detection Object detection YOLO Pipeline defect detection

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automatic defect detection in Sewer Pipe Closed- Circuit Television Images via Improved You Only Look Once Version 5 Object detection Network

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IEEE ACCESS 2024年 12卷 92797-92825页

作者： Huang, Jianying Kang, Hoon Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

A sewer pipe system is an indispensable part of a modern city's infrastructure. However, sewer pipes gradually deteriorate over time and develop defects such as cracks, deposits, and infiltration, which seriously affect their performance. Consequently, routine inspections are crucial for assessing the condition of sewer pipes and ensuring their proper operation. Closed-circuit television (CCTV) is frequently utilized for sewer pipe inspection. Previously, the interpretation of sewer pipe CCTV images relied on professional technologists and traditional computer vision techniques to identify and localize defects. They are inefficient and poorly robust. In this paper, we propose an efficient and robust automatic interpretation technique based on deep learning to identify and localize defects in sewer pipe CCTV images in real time. Specifically, we propose an improved you only look once version 5 large 6 (YOLO v5l6) network based on the original YOLO v5l6 network. The improved YOLO v5l6 network is based on improvements in the following two aspects: (1) We concatenate a convolutional block attention module (CBAM) module into the Bottleneck block of the Cross Stage Partial Bottleneck with 3 Convolutions (C3) module at the end of the original YOLO v5l6 backbone to create a C3CBAM module. (2) Moreover, we concatenate the CBAM into the end of four C3 modules of the original YOLO v5l6 neck to create four C3_CBAM_Att modules. With these improvements, the improved YOLO v5l6 network can significantly reinforce the backbone feature extraction and neck feature fusion to rapidly and accurately detect multiscale, multiclass defects in the presence of interference.

关键词： YOLO Feature extraction Videos TV Inspection defect detection Deep learning Pipelines Sewage treatment Sewer pipe closed-circuit television automatic defect detection deep learning attention mechanism you only look once version 5

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automatic defect detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2018年第2期67卷 257-269页

作者： Chen, Junwen Liu, Zhigang Wang, Hongrui Nunez, Alfredo Han, Zhiwei Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Delft Univ Technol Sect Railway Engn NL-2628 CN Delft Netherlands

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners' defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan-Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.

关键词： automatic defect detection catenary support device deep convolutional neural network (DCNN) fastener high-speed railway

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automatic defect detection IN INFRARED THERMOGRAPHY BY DEEP LEARNING ALGORITHM 42

AUTOMATIC DEFECT DETECTION IN INFRARED THERMOGRAPHY BY DEEP ...

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Conference on Thermosense - Thermal Infrared Applications XLII

作者： Fang, Qiang Nguyen, Ba Diep Castanedo, Clemente Ibarra Duan, Yuxia Maldague, Xavier Univ Laval Comp Vis & Syst Lab Dept Elect & Comp Engn 1065 Av Med Quebec City PQ G1V 0A6 Canada Cent South Univ Sch Phys & Elect 932 Lushan South Rd Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781510635968;9781510635951

Non-destructive Evaluation (NDE) is a field that is used to identify all kinds of structural damage in an object of interest without resulting in any permanent damage or modification to the object. This field has been intensively investigated for many years. Among several research topics in this field, the supervised defect detection methods are among the most innovative and challenging. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. Deep learning has shown its ability to overcome most of the disadvantages suffered by previous existing approaches in a great number of applications. In this paper, we propose a deep learning architecture based on infrared thermography inspection intended to automatically identify defects (including internal and invisible cracks, delamination, etc.) efficiently and accurately. We studied the proposed deep learning algorithms to achieve automatic defect detection and precise localization (subsurface defects case) from different thermal image sequences. To evaluate the efficiency and robustness of the proposed methodology, specimens containing artificial defects were selected for experimental configuration.

关键词： automatic defect detection Infrared Thermography Deep learning algorithms Non-destructive Evaluation (NDE) Supervised learning Image processing

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A Tool for automatic defect detection in Models used in Model-Driven Engineering

A Tool for Automatic Defect Detection in Models used in Mode...

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7th International Conference on the Quality of Information and Communications Technology (QUATIC)

作者： Marin, Beatriz Giachetti, Giovanni Pastor, Oscar Vos, Tanja E. J. Univ Politecn Valencia Ctr Invest Metodos Prod Software ProS Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9780769542416

In the Model-Driven Engineering (MDE) field, the quality assurance of the involved models is fundamental for performing correct model transformations and generating final software applications. To evaluate the quality of models, defect detection is usually performed by means of reading techniques that are manually applied. Thus, new approaches to automate the defect detection in models are needed. To fulfill this need, this paper presents a tool that implements a novel approach for automatic defect detection, which is based on a model-based functional size measurement procedure. This tool detects defects related to the correctness and the consistency of the models. Thus, our contribution lays in the new approach presented and its automation for the detection of defects in MDE environments.

关键词： defect detection Functional Size Measurement MDE automatic defect detection model-driven engineering quality assurance software applications software quality software tools

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automatic defect detection for fabric printing using a deep convolutional neural network

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INTERNATIONAL JOURNAL OF FASHION DESIGN TECHNOLOGY AND EDUCATION 2022年第2期15卷 142-157页

作者： Chakraborty, Samit Moore, Marguerite Parrillo-Chapman, Lisa North Carolina State Univ Dept Text & Apparel Technol & Management Raleigh NC 27606 USA

defect detection is a crucial step in textile and apparel quality control. An efficient defect detection system can ensure the overall quality of the processes and products that are acceptable to consumers. Existing techniques for real-time defect detection tend to vary according to unique manufacturing processes, focal defects and computational algorithms. Although the need is high, research related to automatic printed fabric defect detection processes is not prevalent in academic literatures. This research proposes a novel methodology that demonstrates the application of convolutional neural network (CNN) to classify printing defects based on the fabric images collected from industries. The research also integrated visual geometric group (VGG), DenseNet, Inception and Xception deep learning networks to compare model performance. The results exhibit that the VGG-based models perform better compared to a simple CNN model, suggesting promise for automatic defect detection (ADD) of printed fabrics that can improve profitability in fashion supply chains.

关键词： automatic defect detection printed fabric image classification convolutional neural network model performance

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Proximity based automatic defect detection in quadratic frequency modulated thermal wave imaging

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INFRARED PHYSICS & TECHNOLOGY 2021年 114卷

作者： Tilak, V. Gopi Ghali, V. S. Lakshmi, A. Vijaya Suresh, B. Naik, R. B. Koneru Lakshmaiah Educ Fdn Infrared Imaging Ctr Dept Elect & Commun Engn Vaddeswararn AP India Naval Mat Res Lab Ambernath E Maharashtra India

automatic and reliable anomaly detection without human intervention is a challenging task in thermal wave imaging. For this purpose, various processing algorithms proposed earlier are outperforming one another to exhibit smaller and deeper anomalies. Machine learning-based defect detection approaches are attracting the research community due to their reliable performance on employing over the stimulated thermal response in active thermography. The present article explores the deployment of local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF) algorithms over an experimental carbon fiber reinforced polymer specimen with artificially drilled flat bottom holes of different sizes at different depths to verify the detection capability of these approaches in Quadratic frequency modulated thermal wave imaging even with noisy synthetic data. In addition, a quantitative study has been performed using various thermographic and machine learning based performance metrics.

关键词： Quadratic frequency modulated thermal wave imaging Proximity automatic defect detection Machine learning

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An automatic Fracture defect detection Approach for Current-carrying Rings of Catenary Droppers Using A Multi-task Neural Network 12

An Automatic Fracture Defect Detection Approach for Current-...

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12th International Conference on Advanced Computational Intelligence (ICACI)

作者： Liu Wenqiang Wang Dan Li Yuyang Yang Cheng Wang Hui Liu Zhigang Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142487

Catenary droppers play an essential role in the catenary system. They both connect messenger wires and contact wires to stabilize contact wires and transmit the working current and short-circuit current. And the current-carrying rings of catenary droppers can avoid the direct connect of droppers and dropper clips and reduce electrical faults and arcs when the locomotive draws current through the pantograph. At present, researchers mainly focus on the loosen defect of droppers, but still do not pay attention to the fracture defect of the current-carrying ring of droppers. Therefore, this paper proposes an automatic fracture defect detection approach for the current-carrying ring of droppers using a multi-task neural network. Compared to traditional solutions based on the connected domains, the method based on neural networks is more automated and robust. First, this network consisted of three function head networks of a classification head network, a regression head network, and a mask head network is performed to get a classification score and a segmentation score, respectively. And then, by counting the scores of normal and faulty components, a fault criterion for evaluating the current-carrying ring is proposed. Experiment results show that the proposed method is highly accurate and automatic for the state detection of catenary droppers.

关键词： high-speed railway catenary dropper automatic defect detection multi-task neural networks

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automatic Visible defect detection and Classification System Prototype Development for Iron-and-Steel Works

Automatic Visible Defect Detection and Classification System...

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Global Smart Industry Conference (GloSIC)

作者： Alkapov, Rustem Konyshev, Artem Vetoshkin, Nikita Valkevich, Nikolay Kostenetskiy, Pavel South Ural State Univ Chelyabinsk Russia

ISBN: (纸本)9781538673867

This paper is written as a part of "automatic visible defect detection and classification system (AVDDCS) development for Electrolytic Tinning Unit" project for Iron and Steel Works. As a part of pre-design research, a system prototype was developed. It consists of Preprocessor, Classifier, Server, Database and User interface. The difference between this prototype and analogs is the use of convolutional neural networks to improve the accuracy of the classification of visible defects.

关键词： automatic defect detection automatic defect classification artificial neural networks computer vision

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automatic zipper tape defect detection using two-stage multi-scale convolutional networks

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NEUROCOMPUTING 2021年 422卷 34-50页

作者： Fang, Houzhang Xia, Mingjiang Liu, Hehui Chang, Yi Wang, Liming Liu, Xiyang Xidian Univ Sch Comp Sci & Technol Software Engn Inst Xian 710071 Peoples R China Nanjing Cognit Internet Things Res Inst Nanjing 210001 Peoples R China Artificial Intelligence Res Ctr Pengcheng Lab Shenzhen 518055 Peoples R China

defects inevitably occur during the manufacturing process of the zipper, significantly affecting its value. Zipper inspection is of significant importance in ensuring the quality of the zipper products. Traditional zipper inspection requires skilled inspectors and is labor-intensive, inefficient, and inaccurate. Currently, automated zipper defects inspection with high precision and high efficiency is still very challenging. In this paper, we propose a novel zipper tape defect detection framework based on fully convolutional networks in a two-stage coarse-to-fine cascade manner. For our special application, the zipper tape defects have multi-scale characteristics. Most of the existing deep learning methods have great advantages in detecting the large-scale defects with prominent features, but are prone to fail in detecting the smallscale ones due to their less remarkable features as well as their general location in a large background area. Thus, we propose to detect first the large local context regions containing the small-scale defects using a multi-scale detection architecture with high efficiency, which integrates a new detection branch by fusing the features in the shallow layer into the high-level layer to boost the detection performance of the context regions. Then we finely detect the small-scale defects from the local context regions detected in the first stage, which can be regarded as large-scale objects that are more easily detected. Extensive comparative experiments demonstrate that the proposed method offers a high detection accuracy while still having high detection efficiency compared with the state-of-the-art methods, coupled with good robustness in some complex cases. (c) 2020 Elsevier B.V. All rights reserved.

关键词： automatic defect detection Zipper tape inspection Fully convolutional neural network Feature fusion Multi-scale detection

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