Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Performance Assessment on Corrosion Resistance of Refractory Materials Based on High-temperature Machine Vision Technology

Refractory materials,as the crucial foundational materials in high-temperature industrial processes such as metallurgy and construction,are inevitably subjected to corrosion and penetration from high-temperature media during their service.Traditionally,observing the in-situ degradation process of refractory materials in complex high-temperature environments has presented challenges.Post-corrosion analysis are commonly employed to assess the slag resistance of refractory materials and understand the corrosion mechanisms.However,these methods often lack information on the process under the conditions of thermal-chemical-mechanical coupling,leading to potential biases in the analysis results.In this work,we developed a non-contact high-temperature machine vision technology by the integrating Digital Image Correlation(DIC)with a high-temperature visualization system to explore the corrosion behavior of Al2O3-SiO2 refractories against molten glass and Al2O3-MgO dry ramming refractories against molten slag at different temperatures.This technology enables realtime monitoring of the 2D or 3D overall strain and average strain curves of the refractory materials and provides continuous feedback on the progressive corrosion of the materials under the coupling conditions of thermal,chemical,and mechanical factors.Therefore,it is an innovative approach for evaluating the service behavior and performance of refractory materials,and is expected to promote the digitization and intelligence of the refractory industry,contributing to the optimization and upgrading of product performance.

Automatic Monitoring System for 3-D Deformation of Crustal Fault Based on Laser and Machine Vision

An automatic monitoring method of the 3-D deformation is presented for crustal fault based on laser and machine vision. The laser source and screen are independently set up in the headwall and footwall, the collimated laser beam creates a circular spot on the screen, meanwhile, the industrial camera captures the tiny deformation of the crustal fault by monitoring the change of the spot position. This method significantly reduces the cost of equipment and labor, provides daily sampling to ensure high continuity of data. A prototype of the automatic monitoring system is developed, and a repeatability test indicates that the error of spot jitter can be minimized by consecutive samples. Meanwhile, the environmental correction model is determined to ensure that environmental changes do not disturb the system. Furthermore, the automatic monitoring system has been applied at the deformation monitoring station(KJX02) of China Beishan underground research laboratory, where continuous deformation monitoring is underway.

基于Machine Vision技术的电梯超载自动化检测方法研究

为提高电梯的使用安全性,避免过量的空间占有率导致的故障问题,提出了基于Machine Vision技术的电梯超载自动检测方法。通过电梯内部的摄像头得到影像数据,然后对图像进行切片与去噪处理,以此得到方差图,并提高算法对边缘图像的识别能力,明确前景面积的占有率,通过此方法得到整个电梯空间的占有率数据,从而实现自动化判断电梯是否超载的功能。试验结果表明:此方法可精确计算出电梯的空间占有率,并能够实现自动化监控的目的,具有良好的适用性。

Highly Efficient Back‑End‑of‑Line Compatible Flexible Si‑Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced robotics.Leveraging 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption.Here,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia detection.With experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,respectively.The optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios.Moreover,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision application.To display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory functions.The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

High-precision automatic measurement of two-dimensional geometric features based on machine vision

To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition and working principle are introduced. The mapping relationship between the feature image coordinates and the measuring space coordinates is established. The method of measuring path planning of small field of view （FOV） images is proposed. With the cooperation of the panoramic image of the object to be measured, the small FOV images with high object plane resolution are acquired automatically. Then, the auxiliary measuring characteristics are constructed and the parameters of the features to be measured are automatically extracted. Experimental results show that the absolute value of relative error is less than 0. 03% when applying the cooperative measurement system to gauge the hole distance of 100 mm nominal size. When the object plane resolving power of the small FOV images is 16 times that of the large FOV image, the measurement accuracy of small FOV images is improved by 14 times compared with the large FOV image. It is suitable for high-precision automatic measurement of two-dimensional complex geometric features distributed on large scale parts.

Feature-based sequential partial vision measurement method for large scale machine parts

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

Research on detection of lane based on machine vision

To prevent a vehicle from departing the lane in assistant or automatic steering, real-time vision-based detection of lane is studied. The system architecture, detecting principle and lane model are described. Then the detecting algorithm of the lane image is discussed in detail. In this algorithm, several proper sub-windows in one image are first selected as the processing regions. To every sub-window, by means of such steps as appropriate pre-processing, edge detection and Hough transform, etc., the lane description features are extracted. Experimental results reveal that this detection method is of good real-time, high recognition reliability and strong robustness, etc., which can provide the decision-making foundation for the following automatic or assistant steering to some extent.

An Example of Machine Vision Applied in Printing Quality Checking——Research on the Checking of Printing Quality by Image Processing

The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image are taken as research objects. On the base of the traditional checking methods of printing quality,combining the method and theory of digital image processing with printing theory in the new domain of image quality checking,it constitute the checking system of printing quality by image processing,and expound the theory design and the model of this system. This is an application of machine vision. It uses the high resolution industrial CCD(Charge Coupled Device) colorful camera. It can display the real-time photographs on the monitor,and input the video signal to the image gathering card,and then the image data transmits through the computer PCI bus to the memory. At the same time,the system carries on processing and data analysis. This method is proved by experiments. The experiments are mainly about the data conversion of image and ink limit show of printing.

Development of an automatic monitoring system for rice light-trap pests based on machine vision

Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

Accurate Measurement Method for Tube's Endpoints Based on Machine Vision

Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube＇s endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube＇s endpoints based on machine vision is proposed. First, reflected light on tube＇s surface can be removed by using photometric linearization. Then, based on the optimization model for the tube＇s endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube＇s endpoint are obtained. To confirm the feasibility, ll tubes are processed to remove the reflected light and then the endpoint＇s positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 ram. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube＇s endpoints without any surface treatment or any tools and can realize on line measurement.

Selection for high quality pepper seeds by machine vision and classifiers

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

Quantifying muskmelon fruit attributes with A-TEP-based model and machine vision measurement

In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation （PAR） （A-TEP）, aiming to explore the relationship between muskmelon （Cucumis melo L.） fruit attributes and environmental factors. Muskmelon surface color was described by parameters of red, green, blue, hue, saturation and brightness （HSI）. Three characteristic parameters, gray level co-occurrence matrix （GLCM）, angular second moment （ASM）, entropy, contrast, and the coverage rate were used to describe the process of muskmelon fruit netting formation. ASM was not significant difference during muskmelon fruit growth. The number and deep of netting stripes gradually increased with fruit growth. Coverage rate increased rapidly for 15-30 d after pollination. The vertical and horizontal diameters of muskmelon fruit were followed a logistic curve. And root mean squared errors （RMSE） between the simulated and measured vertical and horizontal diameters were 3.527 and 4.696 mm, respectively. RMSE of red, green, blue, saturation and brightness were 0.999, 2.690, 2.992, 0.033 and 5.51, respectively, and the RMSE for entropy, contrast and coverage rates were 0.077, 0.063 and 0.015, respectively, indicating a well consistent between measured and simulated values.

Recognition of wood surface defects with near infrared spectroscopy and machine vision

To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.

Machine vision inspection of rice seed based on Hough transform

A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

Machine vision system for visual defect inspection of TFT-LCD

To improve the identification for visual defect of TFF-LCD, a new machine vision system is proposed, which is superior to human eye inspection. The system respectively employs a CCD camera to capture the image of TFT-LCD panel and an image processing system to identify potential visual defects. Image pre-processing, such as average filtering and geometric correction, was performed on the captured image, and then a candidate area of defect was segmented from the background. Feature information extracted from the area of interest entered a fuzzy rule-based classifier that simulated the defect inspection of TFT-LCD undertaken by experienced technicians. Experiment results show that the machine vision system can obtain fast, objective and accurate inspection compared with subjective and inaccurate human eye inspection.

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.