Hough Circle Research Articles

A Segmentation-Based Automated Corneal Ulcer Grading System for Ocular Staining Images Using Deep Learning and Hough Circle Transform

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading.

AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples.

Defect Detection of GFRP Composites through Long Pulse Thermography Using an Uncooled Microbolometer Infrared Camera.

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites.

Two deep learning methods in comparison to characterize droplet sizes in emulsification flow processes

AbstractFor reliable supervision in multiphase processes, the droplet size represents a critical quality attribute and needs to be monitored. A promising approach is the use of smart image flow sensors since optical measurement is the most commonly used technique for droplet size distribution determination. For this, two different AI-based object detection methods, Mask RCNN and YOLOv4, are compared regarding their accuracy and their applicability to an emulsification flow process. Iterative optimization steps, including data diversification and adaption of training parameters, enable the models to achieve robust detection performance across varying image qualities and compositions. YOLOv4 shows better detection performances and more accurate results which leads to a wider application window than Mask RCNN in determining droplet sizes in emulsification processes. The final droplet detection model YOLOv4 with Hough Circle (HC) for feature extraction determines reliable droplet sizes across diverse datasets of liquid-liquid flow systems (disperse phase content 1–15 vol.-%, droplet size range 5–150 μm). Evaluating the adjustment of Confidence Scores (CS) ensures statistical representation of even smaller droplets. The droplet detection performance of the final YOLOv4 model is compared with a manual image processing method to validate the model in general as well as its accuracy and reliability. Since YOLOv4 in combination with Hough Circle (HC) shows an accurate and robust detection and size determination, it is applicable for online monitoring and characterization of various liquid-liquid flow processes. Graphical abstract

Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images.

This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases.

Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground

In order to improve the automation and safety of underground steel belt support in coal mines, a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors is proposed. Using STM32F407ZGT6 as the main control chip, tasks such as data acquisition and processing, motor motion control, etc., are assigned based on the real-time operating system FreeRTOS. Using the XY mobile platform equipped with inductive sensors to detect steel belts, The collected data includes coordinate values and voltage values. Adaptive threshold generation and correction strategies are used for threshold segmentation and extraction of anchor hole boundary points. The principle of Hough circle transformation is used to fit the extracted boundary points into circles. The results show that this method can perform anchor hole positioning with a positioning error of within 5 mm, meeting the design requirements.

Semi-automatic Labeling of Satellite Images Using Texture Features and Hough Circle Transformation

Semi-automatic Labeling of Satellite Images Using Texture Features and Hough Circle Transformation

Attitude measurement of ultraclose-range spacecraft based on improved YOLOv5s and adaptive Hough circle extraction.

In order to fulfill the requirements for various operations in space, such as rendezvous, docking, and capturing, there is a pressing need to achieve ultraclose-range spacecraft pose measurement. This paper addresses the challenges of pose measurement under low-light conditions at ultraclose range by introducing a stereovision solution based on target detection and adaptive circle extraction. Initially, an improved target detection algorithm is employed to expedite feature object detection. Subsequently, an adaptive circle extraction algorithm is developed through analysis of camera imaging to surmount challenges related to feature extraction and potential feature loss in the space environment. This approach facilitates swift and accurate measurement of spacecraft at ultraclose range. The results showcase a 66.36% reduction in parameter count for the enhanced target detection algorithm compared with the prevalent YOLOv7_tiny algorithm. Additionally, the adaptive circle extraction algorithm demonstrates an 11.4% increase in cooperative target feature extraction precision compared with existing methods while maintaining requisite detection speed. Simulation experiments indicate that the real-time position measurement error for spacecraft at ultraclose range is less than 0.18mm, and angle measurement error is less than 0.05°. This presents a viable visual solution for spacecraft pose measurement at ultraclose range in low-light environments.

Drainage during Condensation on Microgrooved Biphilic Surfaces.

In this work, we investigate and compare the condensation behavior of hydrophilic, hydrophobic, and biphilic microgrooved silicon samples etched by reactive ion etching. The microgrooves were 25 mm long and 17-19 μm deep with different topologies depending on the etching process. Anisotropically etched samples had 30 μm wide rectangular microgrooves and silicon ridges between them. They were either left hydrophilic or covered with a hydrophobic fluorocarbon or photoresist layer. Anisotropically etched samples consisted of 48 μm wide semicircular shaped microgrooves, 12 μm wide silicon ridges between them, and a 30 μm wide photoresist stripe centered on the ridges. The lateral dimensions were chosen to be much smaller than the capillary length of water to support drainage of droplets by coalescence rather than droplet sliding. Furthermore, to achieve a low thermal resistance of the periodic surface structure consisting of water-filled grooves and silicon ridges, the trench depth was also kept small. The dripped-off total amount of condensate (AoC) was measured for each sample for 12 h under the same boundary conditions (chamber temperature 30 °C, cooling temperature 6 °C, and relative humidity 60%). The maximum increase in AoC of 15.9% (9.6%) against the hydrophilic (hydrophobic) reference sample was obtained for the biphilic samples. In order to elucidate their unique condensation behavior, in situ optical imaging was performed at normal incidence. It shows that the drainage of droplets from the stripe's surface into the microgrooves as well as occasional droplet sliding events are the dominant processes to clear the surface. To rationalize this behavior, the Hough Circle Transform algorithm was implemented for image processing to receive additional information about the transient droplet size and number distribution. Postprocessing of these data allows calculation of the transient water load on the stripe's surface, which shows an oscillatory behavior not previously reported in the literature.

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

AbstractApples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near‐infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full‐band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high‐pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full‐band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1‐score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product quality.Practical applicationsDetecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RGB camera is difficult to detect apple early bruises that are not visible to the naked eye, and hyperspectral imaging has a large amount of data and redundant image data among wavelengths. Compared with previous studies, apple early bruise detection method proposed in this article combining NIR camera imaging technology and adaptive threshold segmentation algorithm can avoid the interference of bright spots, fruit stem and calyx on bruise segmentation while capturing a single channel image, providing a new method for accurate and rapid detection of early bruises on apples.

Infield corn kernel detection using image processing, machine learning, and deep learning methodologies under natural lighting

Machine vision has been increasingly used to address agricultural issues. One such case is corn field harvest losses and image-based object detection approaches, namely image processing, machine learning, and deep learning were investigated to detect and count infield corn kernels, immediately after harvest for combine harvester performance evaluation. A hand-held low-cost RGB camera was used to collect images with kernels of different backgrounds, based on which a 420 images dataset (200, 40, and 180 for training, validation, and testing, respectively) was generated. Three different models for kernel detection were constructed based on image processing, machine learning, and deep learning. For the imaging processing method, the images were preprocessed (color thresholding, graying, and erosion), followed by Hough circle detection to identify kernels. For the machine learning (cascade detector) and deep learning (Mask R-CNN, EfficientDet, YOLOv5, and YOLOX), models were trained, validated, and tested. Experimental results showed the overall performance of the deep learning network YOLOv5 was superior to the other approaches, with a small model size (89.3 MB) and a high model average precision (78.3 %) for object detection. The detection accuracy, undetection rate and F1 value were 90.7 %, 9.3 %, and 91.1 %, respectively, and the average detection rate was 55 fps. This study demonstrates that the YOLOv5 model has the potential to be used as a real-time, reliable, and robust method for infield corn kernel detection.

Evaluation of quantitative ultrasonic C-scan testing for refill friction stir spot welding joints based on time-frequency analysis

This paper focuses on a comparative study of different ultrasonic feature-based C-scan testing imaging methods for characterizing refill friction stir spot welding (RFSSW) joints, and accurately identifying and measuring the nugget boundary. The aim is to explore a suitable method for nugget characterization and automatic size analysis. The research indicates that the frequency-domain C-scan imaging method outperforms the time-domain C-scan imaging method in accurately characterizing the nugget. Furthermore, the C-scan imaging method based on the feature value of the base material zone (BMZ) echo signal performs better than the method based on the nugget echo signal. The tested nugget sizes obtained by using the –6 dB drop-off method to identify the boundaries in the C-scan images are generally larger than the metallographic measurement values. A novel method is proposed in this paper, using the difference in main frequency amplitude values between the BMZ and the nugget as the feature value for the C-scan testing image, and employing the Hough circle transformation for an automatic extraction of joint size. This method achieves improved nugget characterization and higher accuracy in size analysis.

Vision based deformation inspection system for automotive glass using Hough circle detectors

Production of automotive glass with the accurate shape and form is a challenge that fabricates the glass products with properties of adequate transparency and lack of imaging deformations and optical flaws. An auto windscreen with deformation flaws will likely distort the driver’s view of surrounding objects, leading to errors in visual judgment that may be dangerous to other road users. In the traditional method of examining vehicle glass in the manufacturing process, human inspectors perform the bulk of the work. This study proposes a frequency reconstruction method established on computer vision to automatically detect deformation flaws in automotive glass. To quantify the deformation level of an outwardly curving glass product, we exploit the digital imaging of a known standard pattern with base dots through a testing sample to capture a transmitted and reflected deformation image of that sample. Then, the proposed method applies the circular Hough transform voting scheme to find the peak points of the base dots in parameter space and reconstructs an image with the base dots of the captured image. The binary testing image subtracts the binary reconstructed image to obtain a binary difference image that displays the detected deformation areas. Experimental outcomes present that the proposed approach using dots pattern reaches a high 82.76% probability of exactly discriminating deformation flaws and a low 1.14% probability of wrongly investigating regular regions as deformation flaws on transmitted appearances of transpicuous glass.

Development of a vision-based automated hole assembly system with quality inspection

This study proposes a novel mechatronics system capable of automating a peg-in-hole assembly process and inspecting the quality of the assembly with vision. The proposed mechatronics system integrates a customized peg insertion tool, a new assembly mechanism, and a control algorithm to efficiently insert pegs into holes with a tolerance of 200 µm. The system improves assembly performance by utilizing dual cameras and several computer vision techniques, including Contrasted Limited Adaptive Histogram Equalization, Canny Edge Detector, Hough Circle Transform, and YOLOv5. In addition, a real-time statistical quality inspection method is proposed and compared with machine learning-based inspection approaches. Various surface textures and materials of the cylindrical workpiece are used to assess the robustness of the proposed inspection methods. Experimental results demonstrate that the proposed system and quality inspection methods exhibit high performance and adaptability.

Center Deviation Measurement of Color Contact Lenses Based on a Deep Learning Model and Hough Circle Transform.

Ensuring the quality of color contact lenses is vital, particularly in detecting defects during their production since they are directly worn on the eyes. One significant defect is the "center deviation (CD) defect", where the colored area (CA) deviates from the center point. Measuring the extent of deviation of the CA from the center point is necessary to detect these CD defects. In this study, we propose a method that utilizes image processing and analysis techniques for detecting such defects. Our approach involves employing semantic segmentation to simplify the image and reduce noise interference and utilizing the Hough circle transform algorithm to measure the deviation of the center point of the CA in color contact lenses. Experimental results demonstrated that our proposed method achieved a 71.2% reduction in error compared with existing research methods.

An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer’s rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.

SCPNet-based correction of distorted multi-spots for three-dimensional surface measurement of metal cylindrical shaft parts.

Metal cylindrical shaft parts are critical components in industrial manufacturing that require high standards for roundness error and surface roughness. When using the self-developed multi-beam angle sensor (MBAS) to detect metal cylindrical shaft parts, the distorted multi-spots degrade the measurement accuracy due to the nonlinear distortion caused by the metal material's reflective properties and surface roughness. In this study, we propose a spot coordinate prediction network (SCPNet), which is a deep-learning neural network designed to predict spot coordinates, in combination with Hough circle detection for localization. The singular value decomposition (SVD) model is employed to eliminate the tilt error to achieve high-precision, three-dimensional (3D) surface reconstruction of metal cylindrical shaft parts. The experimental results demonstrate that SCPNet can effectively correct distorted multi-spots, with an average error of the spot center of 0.0612 pixels for ten points. The proposed method was employed to measure metal cylindrical shaft parts with radii of 10 mm, 20 mm, 35 mm, and 50 mm, with resulting standard deviation (STD) values of 0.0022 µm, 0.0026 µm, 0.0028 µm, and 0.0036 µm, respectively.

Nut Geometry Inspection Using Improved Hough Line and Circle Methods.

Nuts are the cornerstone of human industrial construction, especially A-grade nuts that can only be used in power plants, precision instruments, aircraft, and rockets. However, the traditional nuts inspection method is to manually operate the measuring instrument for conducting an inspection, so the quality of the A-grade nut cannot be guaranteed. In this work, a machine vision-based inspection system was proposed, which performs a real-time geometric inspection of the nuts before and after tapping on the production line. In order to automatically screen out A-Grade nuts on the production line, there are 7 inspections within this proposed nut inspection system. The measurements of parallel, opposite side length, straightness, radius, roundness, concentricity, and eccentricity were proposed. To shorten the overall detection time regarding nut production, the program needed to be accurate and uncomplicated. By modifying the Hough line and Hough circle, the algorithm became faster and more suitable for nut detection. The optimized Hough line and Hough circle can be used for all measures in the testing process.

Convolutional neural network for automatic detection and characterization of abdominal aortic aneurysm.

We read with great interest the recent publication of Camara et al. on the development of an artificial intelligence (AI)-derived method to enable an automatic detection of abdominal aortic aneurysm (AAA) (1Camara JR TR, Pop A, Matthew BS, Shedd P, Brandon S, Dobrowski BS, Cole J, Knox BS, Abou-Zamzam A, Sharon C, Kiang Journal of Vascular Surgery Cases and Innovative techniquesGoogle Scholar). Using datasets composed of 200 computed tomography angiography (CTA) from patients with AAA and 200 matched control patients with non-aneurysmal infrarenal aorta, the authors developed a method based on convolutional neural network (CNN) and testing demonstrated a robust accuracy of the model (99.1% with an area under the curve of 0.99). Their results point to the interest of such application for the screening of AAA. The VGG-16 neural network architecture was used to develop the AAA detection system and transfer learning was applied to the neural network. It would be interesting to know what was the accuracy of the CNN before transfer learning to show what was the added value of the transfer learning in the pipeline. The use of CNN to classify aneurysmal from non-pathological aortas has been so far poorly reported (2Mohammadi S. Mohammadi M. Dehlaghi V. Ahmadi A. Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm.Cardiovasc Eng Technol. 2019; 10: 490-499Crossref PubMed Scopus (27) Google Scholar). Nevertheless, it is to note that several studies have recently demonstrated the interest of CNN to develop a fully automatic segmentation of AAA (3Lareyre F. Adam C. Carrier M. Raffort J. Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning.J Clin Med. 2021; 10Crossref Scopus (9) Google Scholar, 4Caradu C. Spampinato B. Vrancianu A.M. Berard X. Ducasse E. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.J Vasc Surg. 2021; 74: 246-256 e6Abstract Full Text Full Text PDF Scopus (20) Google Scholar, 5Adam C. Fabre D. Mougin J. Zins M. Azarine A. Ardon R. et al.Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.Eur J Vasc Endovasc Surg. 2021; Abstract Full Text Full Text PDF Scopus (12) Google Scholar). These studies showed a good accuracy of the methods compared to human experts and demonstrated the feasibility to use AI for an automatic measurement of AAA maximal diameter (4Caradu C. Spampinato B. Vrancianu A.M. Berard X. Ducasse E. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.J Vasc Surg. 2021; 74: 246-256 e6Abstract Full Text Full Text PDF Scopus (20) Google Scholar, 5Adam C. Fabre D. Mougin J. Zins M. Azarine A. Ardon R. et al.Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.Eur J Vasc Endovasc Surg. 2021; Abstract Full Text Full Text PDF Scopus (12) Google Scholar, 6Lareyre F. Chaudhuri A. Flory V. Augene E. Adam C. Carrier M. et al.Automatic measurement of maximal diameter of abdominal aortic aneurysm on computed tomography angiography using artificial intelligence.Ann Vasc Surg. 2021; Google Scholar). Hence, CNN offers perspectives to develop applications oriented toward screening and identification of AAA as well as new tools to facilitate its anatomic characterization, which could improve pre-surgical planning and follow-up. As stated by the authors, in addition to develop advanced imaging analysis, ML has the potential to build predictive models of patients’ outcomes and several studies have underlined its interest to better assess AAA growth, risk of rupture, as well as risks of post-operative complications including mortality or re-interventions (7Raffort J. Adam C. Carrier M. Ballaith A. Coscas R. Jean-Baptiste E. et al.Artificial intelligence in abdominal aortic aneurysm.J Vasc Surg. 2020; Abstract Full Text Full Text PDF Scopus (53) Google Scholar, 8Raffort J. Adam C. Carrier M. Lareyre F. Fundamentals in Artificial Intelligence for Vascular Surgeons.Ann Vasc Surg. 2020; 65: 254-260Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 9Lareyre F. Raffort J. Looking for the Optimal Evaluation of Abdominal Aortic Aneurysm Risk of Rupture.J Endovasc Ther. 2020; 27: 345-346Crossref Scopus (0) Google Scholar). AI has the potential to enhance precision medicine and although further studies are required to evaluate the accuracy and external validation, applications for clinical practice can hopefully be expected within the next few years. This work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.

Automatic Detection and Imaging of Rivet Hole Defects for Aircraft Structures With Optimized Sensor Array Using Eddy Current Method and Image Analysis

Fatigue cracking in riveted lap joints on aircraft could cause severe safety consequences in flight. The prior inspection for the integrity of the second layer of aircraft structure with rivet removed is necessary due to the priority of safety. Methods such as radiographic and ultrasonic testing are widely applied but require relatively expensive equipment, mechanical scanning or professional operators. Eddy current method can be adopted for non-destructive testing (NDT) inspection, which is safe and low-cost. In this paper, a hand-held eddy current (EC) sensor array was optimised based on sensitivity map analysis. The EC sensor array was used with a multi-channel EM instrument, which was capable of inspecting defects around the fastener locations covered by the head of rivets. A simulated sample of riveted joint with defects was examined with the eddy current sensing system and the collected data was processed by Hough Circle Transformation (HCT) and texture analysis. The analysis results could detect and locate fatigue cracks on the sample. The presented eddy current sensing system in this paper achieved an automatic, real-time, portable, convenient and accurate inspection method for defects in riveted joints.