Acoustic Non-Destructive Testing Technology in Concrete Bridge Inspection and Pile Foundation Detection

This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Optical generation,detection and non-destructive testing applications of terahertz waves

Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.

Multi-Energy Gamma-Ray Attenuations for Non-Destructive Detection of Hazardous Materials

We present a non-destructive method (NDM) to identify minute quantities of high atomic number (Z) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources 133Ba (half-life = 10.55 y) and 152Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

Perovskite semiconductors for direct X-ray detection and imaging

Halide perovskites have emerged as the next generation of optoelectronic materials and their remarkable performances have been attractive in the fields of solar cells,light-emitting diodes,photodetectors,etc.In addition,halide perovskites have been reported as an attractive new class of X-ray direct detecting materials recently,owning to the strong X-ray stopping capacity,excellent carrier transport,high sensitivity,and cost-effective manufacturing.Meanwhile,perovskite based direct Xray imagers have been successfully demonstrated as well.In this review article,we firstly introduced some fundamental principles of direct X-ray detection and imaging,and summarized the advances of perovskite materials for these purposes and finally put forward some needful and feasible directions.

Ultrasensitive and stable X-ray detection using zero-dimensional lead-free perovskites

Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence.Three-dimensional(3 D)organic-inorganic hybrid lead halide perovskites are promising for direct X-ray detection-they show improved sensitivity compared to conventional X-ray detectors.However,their high and unstable dark current,caused by ion migration and high dark carrier concentration in the 3 D hybrid perovskites,limits their performance and long-term operation stability.Here we report ultrasensitive,stable X-ray detectors made using zero-dimensional(0 D)methylammonium bismuth iodide perovskite(MA3Bi2I9)single crystals.The 0 D crystal structure leads to a high activation energy(Ea)for ion migration(0.46 e V)and is also accompanied by a low dark carrier concentration(~10^6 cm^-3).The X-ray detectors exhibit sensitivity of 10,620μC Gy-1 air cm-2,a limit of detection(Lo D)of 0.62 nG yairs-1,and stable operation even under high applied biases;no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of^23,800 m Gyair,equivalent to>200,000 times the dose required for a single commercial X-ray chest radiograph.Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Lead-free molecular one-dimensional perovskite for efficient X-ray detection

In recent years,great progress has been achieved for organicinorganic halide perovskites due to their excellent optoelectronic properties and stability for photovoltaics,light emitting diodes,and high-energy radiation detection[1-5].One-dimensional(1D)perovskites,as an important derivative of three-dimensional(3D)perovskites,exhibit low exciton dissociation efficiency,which can produce strong quantum confinement and form self-trapping excited state[6],In addition,the hydrophobic properties and the inhibition of ion migration from large organic cations improve the moisture and thermal stability for optoelectronic devices.

Non-destructive study of fruits using grating-based X-ray imaging

Grating-based X-ray imaging can make use of conventional tube sources to provide absorption, refraction and scattering contrast images from a single set of projection images efficiently. In this paper, a fresh cherry tomato and a dried umeboshi are imaged by using X-ray Talbot–Lau interferometer. The seed distribution in the scattering image of the cherry tomato, and the wrinkles of epicarp in the refraction image of the umeboshi, are shown distinctly. The refraction and scattering images provide more information on subtle features than the absorption image. Also, the contrast-to-noise ratio values show distinguishing capacity of the three kinds of imaging techniques. The results confirm that grating-based X-ray imaging is of great potential in non-destructive fruit testing.

X-ray detection based on complementary metal-oxide-semiconductor sensors

Complementary metal-oxide-semiconductor(CMOS) sensors can convert X-rays into detectable signals; therefore, they are powerful tools in X-ray detection applications. Herein, we explore the physics behind X-ray detection performed using CMOS sensors. X-ray measurements were obtained using a simulated positioner based on a CMOS sensor, while the X-ray energy was modified by changing the voltage, current, and radiation time. A monitoring control unit collected video data of the detected X-rays. The video images were framed and filtered to detect the effective pixel points(radiation spots).The histograms of the images prove there is a linear relationship between the pixel points and X-ray energy. The relationships between the image pixel points, voltage, and current were quantified, and the resultant correlations were observed to obey some physical laws.

Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

Exploring centimeter-sized crystals of bismuth-iodide perovskite toward highly sensitive X-ray detection

Lead-halide perovskites exhibit outstanding performance in X-ray detection due to their intrinsic features such as high charge carrier mobility,large atomic number,and long carrier lifetime,but the toxicity of lead is regarded as the major factor hindering their development.Here,we introduce organic molecule(R)-(-)-2-methylpiperazine(R-MPz)into the bismuth-based structure to synthesize lead-free(R)-(H_(2)MPz)BiI_(5)(R-MBI).The high-quality centimeter-sized single crystals have been obtained,which show a low dark current and superior environmental stability.Particularly,the single-crystal device of R-MBI exhibits a highμτproduct up to 1.88×10^(-4)cm^(2)/V and a low trap density of 1.21×10^(10)cm^(-3).Further,the detector displays excellent detection sensitivity of 263.58μC Gy_(air)^(-1)cm^(-2)and a favorable low detection limit of 4.35μGyair/s,both of which meet the requirement for medical diagnostics.These findings shed light on the exploration of innovative bismuth-based hybrid perovskites for high-performance X-ray detection.

A novel 4D resolution imaging method for low and medium atomic number objects at the centimeter scale by coincidence detection technique of cosmic-ray muon and its secondary particles

The muon radiography imaging technique for high-atomic-number objects(Z)and large-volume objects via muon transmission imaging and muon multiple scattering imaging remains a popular topic in the field of radiation detection imaging.However,few imaging studies have been reported on low and medium Z objects at the centimeter scale.This paper presents an imaging system that consists of three layers of a position-sensitive detector and four plastic scintillation detectors.It acquires data by coincidence detection technique of cosmic-ray muon and its secondary particles.A 3D imaging algorithm based on the density of the coinciding muon trajectory was developed,and 4D imaging that takes the atomic number dimension into account by considering the secondary particle ratio information was achieved.The resultant reconstructed 3D images could distinguish between a series of cubes with 5-mm-side lengths and 2-mm-intervals.If the imaging time is more than 20 days,this method can distinguish intervals with a width of 1 mm.The 4D images can specify target objects with low,medium,and high Z values.

Current Trends and Perspectives of Detection and Location for Buried Non-Metallic Pipelines

Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent years,non-metallic pipes,such as plastic pipes,ceramic pipes,and concrete pipes,are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply,drainage,heat,industry,oil,and gas.The location technologies for the location of the buried metal pipeline have become mature,but detection and location technologies for the non-metallic pipelines are still developing.In this paper,current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized.Initially,this paper reviews and analyzes electromagnetic induction technologies,electromagnetic wave technologies,and other physics-based technologies.It then focuses on acoustic detection and location technologies,and finally introduces emerging technologies.Then the technical characteristics of each detection and location method have been compared,with their strengths and weaknesses identified.The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined.Finally,some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.