scholarly journals Nondestructive Evaluation of Special Defects Based on Ultrasound Metasurface

2022 ◽  
Vol 8 ◽  
Author(s):  
Hualiang Zhao ◽  
Chuanxin Zhang ◽  
Jiajie He ◽  
Ying Li ◽  
Boyi Li ◽  
...  
Keyword(s):  
Nondestructive Evaluation ◽  
Defect Detection ◽  
Coupling Efficiency ◽  
Sample Surface ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Planar Configuration ◽  
Directional Emission ◽  
Phase Manipulation ◽  
Thickness Evaluation

We demonstrate the nondestructive evaluation by means of directional ultrasound emitted from a planar metasurface. The ultrasound metasurface is designed to generate the collimated and directional ultrasound efficiently in a planar configuration, which is endowed with the full-2π-range phase manipulation ability and high transmittance up to 80%. We employ the directional emission based on the ultrasound metasurface to innovate the traditional nondestructive evaluation methods, benefited from the freely controlled directivity and the superior fitness to sample surface of the planar metasurface. Merits of this innovative application are evidenced by the remarkable accuracy (higher than 98%) in the thickness evaluation, and precise detection (accuracy higher than 96%) of the special defect inside the V-shaped workpiece which is intractable to be inspected conventionally. The implementation of the metasurface-based directional ultrasound emission in the nondestructive evaluation bears the advantages of high coupling efficiency, superior fitness, high accuracy, and applicability to special defect, providing new solutions to the challenges in conventional defect detection and promotes the development in the nondestructive evaluation applications.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

Measurements of the Spectral and Directional Emission From Microgrooved Silicon Surfaces

1988 ◽  
Vol 110 (3) ◽  
pp. 680-686 ◽  
Author(s):  
P. J. Hesketh ◽  
B. Gebhart ◽  
J. N. Zemel
Keyword(s):  
Sample Surface ◽  
Silicon Surfaces ◽  
Repeat Distance ◽  
Radiation Emission ◽  
Polarization Effects ◽  
Strong Polarization ◽  
Directional Emission ◽  
Organ Pipe ◽  
Temperature Levels ◽  
Phosphorus Doped

This paper reports measurements of both the spectral and specular thermal radiation emission characteristics of very regularly microconfigured grooved surfaces in a silicon substrate at 300 and 400°C. The resulting surfaces were phosphorus-doped, to assure the dominance of the emission from the material near the sample surface. The samples had groove depths H of zero for a reference, to 42 μm, and widths L = 12.6 to 14 μm. The geometry repeat distance was 22 μm, or 455 grooves per cm. The grooves correspond directly in size to the band of principle emission wavelengths λ that arises at these temperature levels. The measurements show strong spectral effects for normal emission, including highly favored frequencies, for H > λ. This suggests a cavity “organ pipe” mode of emission. Similar, though modified, effects were found in directional emission, away from the normal. There also were strong polarization effects, with the cross-groove polarization mode dominant. The spectral and specular measurements are compared with calculations of the classical kind, which tacitly assume that λ < < H = 0(L).

Feature weighting network for aircraft engine defect detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2050012
Author(s):  
Liqiong Chen ◽  
Lian Zou ◽  
Cien Fan ◽  
Yifeng Liu
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Aircraft Engine ◽  
Air Transportation ◽  
Feature Weighting ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Feature Pyramid ◽  
New Feature

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications.

Neighborhood-preserving cross correlation for automated visual inspection of fine-structured textile fabrics

2011 ◽  
Vol 81 (19) ◽  
pp. 2033-2042 ◽  
Author(s):  
A. S. Tolba
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Cross Correlation ◽  
Gabor Filter ◽  
Probabilistic Neural Network ◽  
Computational Cost ◽  
Detection Accuracy ◽  
Automated Visual Inspection ◽  
Textile Fabrics ◽  
Discriminant Power

The automated visual inspection of homogeneous flat surface products is a challenging task that needs fast and accurate algorithms for defect detection and classification in real time. Multi-directional and Multi-scale approaches, such as Gabor Filter Banks and Wavelets, have high computational cost in addition to their average performance in defect characterization. This paper presents a novel implementation of a neighborhood-preserving approach for the fast and accurate inspection of fine-structured industrial products using a new neighborhood-preserving cross-correlation feature vector. The fast and noise immune Probabilistic Neural Network (PNN) classifier has been found to be very suitable for defect detection in homogeneous non-patterned surfaces with acceptable slight variations, such as textile fabrics. A defect detection accuracy of 99.87% has been achieved with 99.29% recall/sensitivity and 99.91% specificity. The discriminant power shows how well the PNN classifier discriminates between normal and abnormal surfaces. The experimental results show that the proposed system outperforms the Gabor function-based techniques.

scholarly journals Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing

2019 ◽  
Vol 10 (1) ◽  
pp. 235 ◽  
Author(s):  
Hongyao Shen ◽  
Wangzhe Du ◽  
Weijun Sun ◽  
Yuetong Xu ◽  
Jianzhong Fu
Keyword(s):  
Feature Extraction ◽  
3D Printing ◽  
Defect Detection ◽  
Point Cloud ◽  
Fused Deposition Modeling ◽  
Surface Defects ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Fused Deposition ◽  
Detection Technology

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.

scholarly journals Nondestructive Evaluation of FRP-Concrete Interface Bond due to Surface Defects

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Nur Yazdani ◽  
Eyosias Beneberu ◽  
Mina Riad
Keyword(s):  
Nondestructive Evaluation ◽  
Surface Defects ◽  
Sample Surface ◽  
Small Scale ◽  
Bond Quality ◽  
Related Factors ◽  
Cfrp Laminates ◽  
Surface Wetness ◽  
Externally Bonded ◽  
Ground Penetrating

Carbon fiber-reinforced polymer (CFRP) laminates have been successfully used as externally bonded reinforcements for retrofitting, strengthening, and confinement of concrete structures. The adequacy of the CFRP-concrete bonding largely depends on the bond quality and integrity. The bond quality may be compromised during the CFRP installation process due to various factors. In this study, the effect of four such construction-related factors was assessed through nondestructive evaluation (NDE) methods, and quantification of the levels of CFRP debonding was achieved. The factors were surface cleanliness, surface wetness, upward vs. downward application, and surface voids. A common unidirectional CFRP was applied to small-scale concrete samples with factorial combinations. Ground-penetrating radar and thermography NDE methods were applied to detect possible disbonds at CFRP-concrete interfaces. Thermography was found to clearly detect all four factors, while the GPR was only effective for detecting the surface voids only. The thermal images overpredicted the amount of debonded CFRP areas by about 25%, possibly due to scaling errors between the thermograph and the sample surface. The maximum debonded CFRP area in any sample was about two percent of the total CFRP area. This is a negligible amount of debonding, showing that the factors considered are unlikely to significantly affect the laminate performance or any CFRP contribution to the concrete member strength or confinement.

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

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