Image Recognition Algorithm Research Articles

A grid management system for COVID-19 antigen detection based on image recognition

ObjectiveTo develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by “health information technology” and “neural network image recognition”, so as to give full play to the advantages of “grid management”. This system is applied to the normalized prevention and control of COVID-19 epidemic. MethodsThe model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. ResultsThe proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. ConclusionsAntigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.

Coal Gangue Recognition during Coal Preparation Using an Adaptive Boosting Algorithm

The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were extracted to construct a feature vector, establishing the training dataset and test dataset. Selection of the SVM kernel function, the GA optimization parameter setting, and the base classifier number was discussed. The coal gangue image recognition effects of the AdaB-GA-SVM classifier and the other strong classifiers with different base SVM classifiers were investigated. The results indicated that the recognition accuracy of GA-SVM was the best when the kernel function of SVM was RBF and the population number, crossover probability, and mutation probability were 80, 0.9, and 0.005, respectively. The AdaB-GA-SVM classifier has excellent identification and effective classification performance with the highest accuracy of 95%, a precision rate of 92.8%, recall rate of 97.3%, and KS values of 0.79.

Virtual Musical Instruments with Python and OpenCV

There is an increasing need for musical aspirants to have access to cheaper musical instruments. This study explores the opportunities to utilize image recognition algorithms via OpenCV to port this technology into readily available modern devices, which will enable inexpensive yet authentic methods of playing a piano. Through OpenCV and Pygame libraries, one can set up a rigid camera that will trace the player’s fingers. The fingers if they cross or hover over a specific coordinate of a key, the piano note (.wav file) will be played by Pygame’s mixer module. This simple yet inexpensive option might help first-time musical aspirants experience music in an affordable and accessible way. Furthermore, this article explores the future scope of accommodating other musical instruments.

Quality Assessment Algorithm of X-Ray Images in Overall Girth Welds Based on Deep Neural Network

Defect recognition and assessment are essential technologies for the safety of pipelines. Recently, deep neural network methods have shown great progress in recognizing pipeline weld defects. However, the current methods cannot achieve online quality assessment for overall girth welds. To solve this problem, this paper proposes a novel quality assessment algorithm for defect recognition and assessment of X-ray girth weld images. First, a two-stage defect classifier based on residual neural network (ResNet) is proposed, which consists of a ResNet18 region classifier and a ResNet50 defect classifier. It can make up for the lack of a single classifier so that the performance and speed of defect recognition can be improved. Second, the feature-extraction calculator is used to extract and calculate the area of the weld defects based on the smallest bounding rectangle method. Third, we propose a quality evaluator based on an improved ensemble learning algorithm and long short-term memory (LSTM), so that the girth welds can be well assessed. Finally, an experiment is conducted using pipeline girth weld X-ray images. The analytic hierarchy process (AHP) method is used to comprehensively assess the level of overall girth-weld defects. The experimental results show that the proposed method is effective in recognizing and evaluating the girth-weld defects of X-ray images. And the accuracy of the quality evaluator can reach 92% for the quality assessment of overall girth welds, compared with manual quality decisions. It effectively achieves online quality assessment for girth welds and has higher evaluation accuracy.

Analysis of Transient Characteristics of Induced Polarization in Innovative Mineral Exploration Solutions (from Case Studies of Gold Deposits)

Abstract—The paper discusses current state of the spectral approach to analysis of induced polarization (IP), its possibilities and limitations, along with directions for the method development. Reviewed are the main methods and approaches for estimation of spectral IP parameters and their specific features. Our practical experiments have shown the advantages of analysis of transient IP characteristics over standard approaches to the IP data analysis and processing. Results of the standard and spectral approaches application to analysis of IP data when solving mineral exploration problems at the Maletoyvayam gold deposit (Kamchatka Peninsula) and the Yasnoe gold occurrence (Taimyr Peninsula) are compared. Here, we demonstrate that low efficiency of works is erroneously regarded as one of the main factors hindering a broad adoption of the spectral IP method in mineral exploration activities. Rather, it is a lack of automated techniques for analysis of transient IP characteristics. We propose a new approach to spectral analysis of IP data, based on the algorithms for image recognition of a reference object, and describe pioneering efforts of its practical application.

Multilabel Recognition Algorithm With Multigraph Structure

In multi-label recognition tasks, convolutional neural networks (CNNs) can extract rich features of images in Euclidean space. However, CNNs struggle to handle dependencies between multi-labels in non-Euclidean space. Graph Convolutional Networks (GCNs) have natural advantages in processing the data in non-Euclidean space. Therefore, many excellent algorithms used GCN to capture the deep dependencies among multi-labels. However, these algorithms directly applied GCN for label feature fusion, leading to nodes with background features affecting the quality of the representation of their neighbor nodes. For example, during label feature smoothing, the representation of nodes with weak edge information tends to converge to nodes with background features. We propose a multi-label image recognition algorithm with a multi-graph structure to address the impact of background features on recognition performance. In brief, when smoothing the dependencies among labels using GCN, we transform the shared adjacency matrix into a combination of multiple adjacency matrices. Unlike node features sharing an adjacency matrix, the multi-graph structure can calculate the performance of each feature carried by the nodes separately during feature fusion and convey more appropriate information rather than in a generalized manner. We have conducted extensive experiments on two multi-label image recognition datasets and have achieved the state-of-the-art.

Analysis of fragments larger than 2 mm generated by a picosatellite fragmentation experiment

Satellite breakup models rely on laboratory tests and in-space collision observations; current models can match fragments distributions generated by traditional satellites but may need to be improved for small spacecraft and modern satellites employing new configurations and materials. In the last years, ground tests have been employed to assess the influence of dimensions, materials and internal configurations on fragments distributions and to define the limits of the current models. In this context, an impact test was performed at the impact facility of the University of Padova to characterize the fragmentation of a picosatellite mock-up; more than 7000 fragments were collected, classified and analysed with automatic image recognition algorithms. It was observed that the experimental characteristic length distribution is line with the prediction of the NASA SBM even for the smallest size classes, while the fragments shape distribution is strongly affected by the materials employed in the picosatellite manufacturing.The subset of the collected fragments larger than 2 mm was recently subjected to a more detailed analysis: each fragment was individually weighed, and its three main dimensions were measured. In this paper, resulting fragments distributions are compared with literature data and the NASA Standard Breakup Model; in particular, an analytic relation between fragments characteristic length and size is found. In addition, results show that characteristic length and area-to-mass distributions are affected by the target materials and are clearly influenced by the size resolution of the analysed fragments.

Fatigue failure mechanism of aluminium alloy riveted single-shear lap joints

The fatigue life of the joint, the evolution process of the contact interface damage and the fatigue fracture behaviour of the joint were studied through a fatigue test of an aluminium alloy single-shear lap joint riveted by a swage-locking pin. Combined with optical microscope (OM), scanning electron microscope (SEM), electron probe microanalyzer (EPMA) and three-dimensional white light interferometry (WLI) and other equipment, and using computer image recognition algorithm, the characteristics and area of debris at the interface of the lap-plate under different cycles were studied, and the damage evolution process of the contact area was revealed. Results show two fatigue failure modes of the joint under different fatigue loads. The contact at the interface is non-uniform, and two morphologies are observed in the contact area: wear and non-wear areas. As the cycles increased from 130,000 to 230,000, the debris area in the contact area increased by 22%, while that in the non-contact area increased by 152%. Micro-cracks are generated in the contact area under the action of fretting wear, and the highest maximum principal stress exists at the crack source.

Algorithm for recognition of images of agricultural crops

Remote sensing includes all types of non-contact. surveys that are carried out from various measuring platforms. The tasks in this area are the following: inventory of agricultural land, control of the state of crops, forecasting yields. The aim of the work is to classify 6 types of crop images (wheat, rice, sugarcane, corn, cotton and jute) with greater accuracy. The paper considers an algorithm for primary processing and recognition of images of agricultural crops and algorithms for constructing a neural network for initial processing and recognition of images to solve problems of noise elimination, minimization, smoothing, normalization, segmentation and image recognition.

Research on visualisation algorithm of handwritten digital image recognition based on deep neural network

Research on visualisation algorithm of handwritten digital image recognition based on deep neural network

Research on visualisation algorithm of handwritten digital image recognition based on deep neural network

Research on visualisation algorithm of handwritten digital image recognition based on deep neural network

Picture Processing Optimization Technology Based on Mask R-CNN Algorithm

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages.

Virtual Navigation and Digital Twin Simulation Tools, Internet of Things-based Decision Support and Big Data Computing Systems, and Image Recognition and Geolocation Data Processing Algorithms in the Virtual Economy of the Metaverse

Virtual Navigation and Digital Twin Simulation Tools, Internet of Things-based Decision Support and Big Data Computing Systems, and Image Recognition and Geolocation Data Processing Algorithms in the Virtual Economy of the Metaverse

Fish sonar image recognition algorithm based on improved YOLOv5.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

Stain-Detection and Anti-Stain Algorithms Based on Dual Detector Data Interchange in Image-Type Displacement Measurement Technology

Displacement measurement technology based on image recognition algorithms represents a novel high-performance technique. In such measurements, when there are stains contaminating the measuring light path, it causes recognition errors from the linear array image sensor on the grating, resulting in inaccurate measurement results. Therefore, this report proposes stain-detection and anti-stain algorithms based on image recognition. The principle of image-type displacement measurement technology is described, and the influence of stains on the displacement measurements is analyzed according to four criteria in order to judge whether there are stains present. Then, an anti-stain algorithm based on the data exchange of dual-image sensors is proposed. Finally, experimental verifications are carried out. The experimental results show that the proposed algorithm effectively identifies the errors in the displacement measurements, when the experimental device is contaminated, the developed algorithms can accurately self-correct for the stain’s interference. The results presented herein provide a theoretical basis for developing highly-reliable displacement measurement devices.

Research on a feature fusion-based image recognition algorithm for facial expression

Research on a feature fusion-based image recognition algorithm for facial expression

The Sentinel Bait Station: an automated, intelligent design pest animal baiting system

Context Ground baiting is a strategic method for reducing vertebrate pest populations. Best practice involves maximising bait availability to the target species, although sustaining this availability is resource intensive because baits need to be replaced each time they are taken. This study focused on improving pest population management through the novel baiting technique outlined in this manuscript, although there is potential use across other species and applications (e.g. disease management). Aims To develop and test an automated, intelligent, and semi-permanent, multi-bait dispenser that detects target species before distributing baits and provides another bait when a target species revisits the site. Methods We designed and field tested the Sentinel Bait Station, which comprises a camera trap with in-built species-recognition capacity, wireless communication and a dispenser with the capacity for five baits. A proof-of-concept prototype was developed and validated via laboratory simulation with images collected by the camera. The prototype was then evaluated in the field under real-world conditions with wild-living canids, using non-toxic baits. Key results Field testing achieved 19 automatically offered baits with seven bait removals by canids. The underlying image recognition algorithm yielded an accuracy of 90%, precision of 83%, sensitivity of 68% and a specificity of 96% throughout field testing. The response time of the system, from the point of motion detection (within 6–10 m and the field-of-view of the camera) to a bait being offered to a target species, was 9.81 ± 2.63 s. Conclusion The Sentinel Bait Station was able to distinguish target species from non-target species. Consequently, baits were successfully deployed to target species and withheld from non-target species. Therefore, this proof-of-concept device is able to successfully provide baits to successive targets from secure on-board storage, thereby overcoming the need for daily bait replacement. Implications The proof-of-concept Sentinel Bait Station design, together with the findings and observations from field trials, confirmed the system can deliver multiple baits and increase the specificity in which baits are presented to the target species using artificial intelligence. With further refinement and operational field trials, this device will provide another tool for practitioners to utilise in pest management programs.

Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

The mechanization and intelligentization of the production process are the main trends in research and development of agricultural products. The realization of an unmanned and automated picking process is also one of the main research hotspots in China’s agricultural product engineering technology field in recent years. The development of automated apple‐picking robot is directly related to imaging research, and its key technology is to use algorithms to realize apple identification and positioning. Aiming at the problem of false detection and missed detection of densely occluded targets and small targets by apple picking robots under different lighting conditions, two different apple recognition algorithms are selected based on the apple shape features to study the traditional machine learning algorithm: histogram of oriented gradients + support vector machine (HOG + SVM) and a fast recognition method for multiple apple targets in a complex occlusion environment based on improved You‐Only‐Look‐Once‐v5 (YOLOv5). The first is the improvement of the CSP structure in the network. Using parameter reconstruction, the convolutional layer (Conv) and the batch normalization (BN) layer in the CBL (Conv + BN + Leaky_relu activation function) module are fused into a batch‐normalized convolutional layer Conv_B. Subsequently, the CA (coordinate attention) mechanism module is embedded into different network layers in the improved designed backbone network to enhance the expressive ability of the features in the backbone network to better extract the features of different apple targets. Finally, for some targets with overlapping occlusions, the loss function is fine‐tuned to improve the model’s ability to recognize occluded targets. By comparing the recognition effects of HOG + SVM, Faster RCNN, YOLOv6, and baseline YOLOv5 on the test set under complex occlusion scenarios, the F1 value of this method was increased by 13.47%, 6.01%, 1.26%, and 3.63%, respectively, and the F1 value of this method was increased by 19.36%, 13.07%, 1.61%, and 4.27%, respectively, under different illumination angles. The average image recognition time was 0.27 s faster than that of HOG + SVM, 0.229 s faster than that of Faster RCNN, and 0.006 s faster than that of YOLOv6. The method is expected to provide a theoretical basis for apple‐picking robots to choose a pertinent image recognition algorithm during operation.

A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir

There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development.

Сравнение применения сверточной нейронной сети и алгоритма -ближайших соседей для распознавания продуктов питания на изображениях

Важную роль в повседневной жизни играет еда, и осведомленность населения о качестве продуктов питания повысилась. Доступность многих видов продуктов питания затрудняет выбор правильного типа здоровой пищи для потребления. Алгоритмы сверточной нейронной сети (CNN) и k-ближайших соседей (KNN) можно использовать для создания моделей классификации и идентификации, включая идентификацию пищевых продуктов. Поэтому для поддержания здорового питания нужна система, которая может быстро определить тип потребляемой пищи и рассчитать ее калорийность. Необходимо создать наилучшую модель идентификации на основе показателей качества модели для точности, предсказания, отзыва и оценки F1, которые будут использоваться для идентификации пищевых продуктов с использованием алгоритмов CNN и KNN. Этот метод исследования извлекает входные данные изображения еды с использованием модели HSV (тон, насыщенность и значение цвета). Данные классифицируются с использованием алгоритмов CNN и KNN. Моделирование выполняется с использованием 900 изображений продуктов питания. Данные разделены на две категории, а именно обучающая и тестовая выборки, в пропорции 75 и 25 % соответственно. Алгоритм KNN тестировался с k = 3, 5 и 7 и сравнивался с CNN. На основании проведенных экспериментов установлено, что метод CNN лучше, чем алгоритм KNN. Есть два класса типов продуктов питания, прогноз по которым неверен, в то время как метод CNN предсказывает только один класс продуктов питания как неправильный. На это указывает точность метода CNN, которая на 5 % лучше, чем метода KNN(3). Точность метода CNN составляет 94 %, а метода KNN(3) - 89 %. Значение F1-оценки для метода CNN равно 0.94, а для метода KNN(3) - 0.89. CNN позволяет модели давать среднюю точность 87.7 %, точность 86.89 %, полноту (recall) 86.89 % и F1 86.33 %. По результатам исследования модель, сформированная с использованием CNN, является лучшей моделью идентификации пищевых продуктов. We thank Universitas Negeri Medan for providing funds to implement this research. The rector, LPPM, the dean of FMIPA Universitas Negeri Medan, and the head of the department have provided support and facilities for the implementation of this research series, especially the rector, LPPM. Although not perfect, hopefully, the results of this research will be helpful in the development of knowledge useful for people.