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Sensing and Imaging for Defect Detection

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 20702

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Prof. Dr. Haitao Wang

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College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: NDT&E technology with ultrasonic, electromagnetic; imaging processing technology; high-imaging-resolution technology
Special Issues, Collections and Topics in MDPI journals

Dr. Yongkai Zhu

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College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: sensor technology; structural health monitoring technology
Special Issues, Collections and Topics in MDPI journals

Dr. Fei Fei

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College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: sensor technology; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the progress made in science and technology, the quality of people's material and cultural lives has improved, meaning that the requirements for product quality and nondestructive testing technology have further increased. Nondestructive testing technology usually includes five conventional testing technologies, namely eddy current, penetration, magnetic particle, ultrasonic, and X-ray, along with their related new technologies. Usually, different materials need to be detected, and the defects that need to be detected are not the same—for example, for metals, defects include non-metallic pipe defects and slag inclusions, the metal not being welded through, porosity, etc.; for the power transmission of porcelain bottles such as ceramic materials, defects include cracks, porosity, etc.

This Special Issue calls for papers aimed at the detection of the most common defects, including surface defects, subsurface defects and so on. Recent advances in sensor technologies form the basis of the development of nondestructive testing technology, data acquirement processing, and image processing technology.

The editors welcome the submission of high-quality research papers not previously published in other journals as well as review articles discussing recent advancements in the development of sensing and imaging techniques for defect detection technology that can be easily used in the NDT&E field.

Prof. Dr. Haitao Wang
Dr. Yongkai Zhu
Dr. Fei Fei
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

sensors NDT&E technology
defect detection technology
imaging technology
data acquirement and processing
sensing techniques

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Related Special Issue

Sensing and Imaging for Defect Detection: 2nd Edition in Sensors (1 article)

Published Papers (11 papers)

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Research

15 pages, 31440 KiB

Open AccessArticle

Structural Plastic Damage Warning and Real-Time Sensing System Based on Cointegration Theory

by Qiang Gao, Junzhou Huo, Youfu Wang, Xiaotian Wang and Chongru Wang

Sensors 2024, 24(18), 5961; https://doi.org/10.3390/s24185961 - 13 Sep 2024

Viewed by 639

Abstract

Structural damage can affect the long-term operation of equipment. Real-time damage warning for structures can effectively avoid accidents caused by structural damage. In this paper, a real-time warning method of structural plastic damage based on the cointegration theory is proposed. This method calculates the cointegration relationship between the strain signals at different measuring points, and the stability of the strain signal relationships is also evaluated. The problem of inaccurate detection caused by the error of strain measurement and environmental influence can be eliminated by the comprehensive judgment of strain between asymmetrical measuring points. A real-time damage sensing system is developed in this paper. In order to improve the real-time and practicability of the system, this paper proposes and determines the residual warning coefficient by analyzing the proportion of the strain residuals exceeding the residual threshold. The research on this sensing system has certain value for the engineering application of damage monitoring methods. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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17 pages, 2514 KiB

Open AccessArticle

A Water Environment-Based Simulated Method for Ultrasonic Testing of Slag Inclusion Weld Defects Based on Improved VMD

by Jing Zhang, Guocai Zhang, Zijie Chen, Hailin Zou, Shuai Xue, Jianjie Deng and Jianqing Li

Sensors 2024, 24(13), 4199; https://doi.org/10.3390/s24134199 - 28 Jun 2024

Viewed by 739

Abstract

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water–slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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12 pages, 5039 KiB

Open AccessCommunication

Improved YOLOv8-Based Target Precision Detection Algorithm for Train Wheel Tread Defects

by Yu Wen, Xiaorong Gao, Lin Luo and Jinlong Li

Sensors 2024, 24(11), 3477; https://doi.org/10.3390/s24113477 - 28 May 2024

Cited by 3 | Viewed by 1114

Abstract

Train wheels are crucial components for ensuring the safety of trains. The accurate and fast identification of wheel tread defects is necessary for the timely maintenance of wheels, which is essential for achieving the premise of conditional repair. Image-based detection methods are commonly used for detecting tread defects, but they still have issues with the misdetection of water stains and the leaking of small defects. In this paper, we address the challenges posed by the detection of wheel tread defects by proposing improvements to the YOLOv8 model. Firstly, the impact of water stains on tread defect detection is avoided by optimising the structure of the detection layer. Secondly, an improved SPPCSPC module is introduced to enhance the detection of small targets. Finally, the SIoU loss function is used to accelerate the convergence speed of the network, which ensures defect recognition accuracy with high operational efficiency. Validation was performed on the constructed tread defect dataset. The results demonstrate that the enhanced YOLOv8 model in this paper outperforms the original network and significantly improves the tread defect detection indexes. The average precision, accuracy, and recall reached 96.95%, 96.30%, and 95.31%. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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17 pages, 4379 KiB

Open AccessArticle

Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection

by Aili Liang, Qishan Wang and Xiaofeng Wu

Sensors 2024, 24(11), 3430; https://doi.org/10.3390/s24113430 - 26 May 2024

Viewed by 819

Abstract

Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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19 pages, 7720 KiB

Open AccessArticle

Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO

by Qian Zhang, Jianping Peng, Kang Tian, Ai Wang, Jinlong Li and Xiaorong Gao

Sensors 2024, 24(5), 1555; https://doi.org/10.3390/s24051555 - 28 Feb 2024

Viewed by 1317

Abstract

In the context of defect detection in high-speed railway train wheels, particularly in ultrasonic-testing B-scan images characterized by their small size and complexity, the need for a robust solution is paramount. The proposed algorithm, UT-YOLO, was meticulously designed to address the specific challenges presented by these images. UT-YOLO enhances its learning capacity, accuracy in detecting small targets, and overall processing speed by adopting optimized convolutional layers, a special layer design, and an attention mechanism. This algorithm exhibits superior performance on high-speed railway wheel UT datasets, indicating its potential. Crucially, UT-YOLO meets real-time processing requirements, positioning it as a practical solution for the dynamic and high-speed environment of railway inspections. In experimental evaluations, UT-YOLO exhibited good performance in best recall, [email protected] and [email protected]:0.95 increased by 37%, 36%, and 43%, respectively; and its speed also met the needs of real-time performance. Moreover, an ultrasonic defect detection data set based on real wheels was created, and this research has been applied in actual scenarios and has helped to greatly improve manual detection efficiency. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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24 pages, 5947 KiB

Open AccessArticle

FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization

by Zhiqian Jiang, Yu Zhang, Yong Wang, Jinlong Li and Xiaorong Gao

Sensors 2024, 24(5), 1368; https://doi.org/10.3390/s24051368 - 20 Feb 2024

Cited by 2 | Viewed by 3233

Abstract

In recent years, a multitude of self-supervised anomaly detection algorithms have been proposed. Among them, PatchCore has emerged as one of the state-of-the-art methods on the widely used MVTec AD benchmark due to its efficient detection capabilities and cost-saving advantages in terms of labeled data. However, we have identified that the PatchCore similarity principal approach faces significant limitations in accurately locating anomalies when there are positional relationships between similar samples, such as rotation, flipping, or misaligned pixels. In real-world industrial scenarios, it is common for samples of the same class to be found in different positions. To address this challenge comprehensively, we introduce Feature-Level Registration PatchCore (FR-PatchCore), which serves as an extension of the PatchCore method. FR-PatchCore constructs a feature matrix that is extracted into the memory bank and continually updated using the optimal negative cosine similarity loss. Extensive evaluations conducted on the MVTec AD benchmark demonstrate that FR-PatchCore achieves an impressive image-level anomaly detection AUROC score of up to 98.81%. Additionally, we propose a novel method for computing the mask threshold that enables the model to scientifically determine the optimal threshold and accurately partition anomalous masks. Our results highlight not only the high generalizability but also substantial potential for industrial anomaly detection offered by FR-PatchCore. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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24 pages, 15581 KiB

Open AccessArticle

Anomaly Detection Based on a 3D Convolutional Neural Network Combining Convolutional Block Attention Module Using Merged Frames

by In-Chang Hwang and Hyun-Soo Kang

Sensors 2023, 23(23), 9616; https://doi.org/10.3390/s23239616 - 4 Dec 2023

Cited by 6 | Viewed by 2977

Abstract

With the recent rise in violent crime, the real-time situation analysis capabilities of the prevalent closed-circuit television have been employed for the deterrence and resolution of criminal activities. Anomaly detection can identify abnormal instances such as violence within the patterns of a specified dataset; however, it faces challenges in that the dataset for abnormal situations is smaller than that for normal situations. Herein, using datasets such as UBI-Fights, RWF-2000, and UCSD Ped1 and Ped2, anomaly detection was approached as a binary classification problem. Frames extracted from each video with annotation were reconstructed into a limited number of images of

3 \times 3

4 \times 3

4 \times 4

5 \times 3

sizes using the method proposed in this paper, forming an input data structure similar to a light field and patch of vision transformer. The model was constructed by applying a convolutional block attention module that included channel and spatial attention modules to a residual neural network with depths of 10, 18, 34, and 50 in the form of a three-dimensional convolution. The proposed model performed better than existing models in detecting abnormal behavior such as violent acts in videos. For instance, with the undersampled UBI-Fights dataset, our network achieved an accuracy of 0.9933, a loss value of 0.0010, an area under the curve of 0.9973, and an equal error rate of 0.0027. These results may contribute significantly to solve real-world issues such as the detection of violent behavior in artificial intelligence systems using computer vision and real-time video monitoring. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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23 pages, 21780 KiB

Open AccessArticle

CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses

by Pawat Chunhachatrachai and Chyi-Yeu Lin

Sensors 2023, 23(23), 9610; https://doi.org/10.3390/s23239610 - 4 Dec 2023

Cited by 1 | Viewed by 2402

Abstract

Automated optical inspection (AOI) plays a pivotal role in the quality control of contact lenses, safeguarding the safety and integrity of lenses intended for both medical and cosmetic applications. As the role of computer vision in defect detection expands, our study probes its effectiveness relative to traditional methods, particularly concerning subtle and irregular defects on the lens rim. In this research study, we propose a novel algorithm designed for the precise and automated detection of rim defects in contact lenses called “CLensRimVision”. This algorithm integrates a series of procedures, including image preprocessing, circle detection for identifying lens rims, polar coordinate transformation, setting defect criteria and their subsequent detection, and, finally, visualization. The method based on these criteria can be adapted either to thickness-based or area-based approaches, suiting various characteristics of the contact lens. This approach achieves an exemplary performance with a 0.937 AP score. Our results offer a richer understanding of defect detection strategies, guiding manufacturers and researchers towards optimal techniques for ensuring quality in the contact lens domain. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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24 pages, 9525 KiB

Open AccessArticle

Design and Implementation of a Hardware and Software System for Visual Assessment of Bituminous Coating Quality

by Dmitrii Kamianskii, Alexander Boldyrev, Nikita Vezdenetsky, Irina Vatamaniuk and Marina Bolsunovskaya

Sensors 2023, 23(23), 9325; https://doi.org/10.3390/s23239325 - 22 Nov 2023

Cited by 1 | Viewed by 961

Abstract

Typically, the quality of the bitumen adhesion in asphalt mixtures is assessed manually by a group of experts who assign subjective ratings to the thickness of the residual bitumen coating on the gravel samples. To automate this process, we propose a hardware and software system for visual assessment of bituminous coating quality, which provides the results both in the form of a discrete estimate compatible with the expert one, and in a more general percentage for a set of samples. The developed methodology ensures static conditions of image capturing, insensitive to external circumstances. This is achieved by using a hardware construction designed to provide capturing the samples at eight different illumination angles. As a result, a generalized image is obtained, in which the effect of highlights and shadows is eliminated. After preprocessing, each gravel sample independently undergoes surface semantic segmentation procedure. Two most relevant approaches of semantic image segmentation were considered: gradient boosting and U-Net architecture. These approaches were compared by both stone surface segmentation accuracy, where they showed the same 77% result and the effectiveness in determining a discrete estimate. Gradient boosting showed an accuracy 2% higher than the U-Net for it and was thereby chosen as the main model when developing the prototype. According to the test results, the evaluation of the algorithm in 75% of cases completely coincided with the expert one, and it had a slight deviation from it in another 22% of cases. The developed solution allows for standardizing the data obtained and contributes to the creation of an interlaboratory digital research database. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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21 pages, 15269 KiB

Open AccessArticle

Anomaly Detection in Annular Metal Turning Surfaces Based on a Priori Information and a Multi-Scale Self-Referencing Template

by Xinyu Suo, Jie Zhang, Jian Liu, Dezhi Yang and Feitao Zhou

Sensors 2023, 23(15), 6807; https://doi.org/10.3390/s23156807 - 30 Jul 2023

Cited by 1 | Viewed by 1411

Abstract

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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15 pages, 4066 KiB

Open AccessCommunication

Wheel Defect Detection Using a Hybrid Deep Learning Approach

by Khurram Shaikh, Imtiaz Hussain and Bhawani Shankar Chowdhry

Sensors 2023, 23(14), 6248; https://doi.org/10.3390/s23146248 - 8 Jul 2023

Cited by 6 | Viewed by 3578

Abstract

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques’ cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection)

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