Research Of Image Recognition Research Articles

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

The intelligent football players’ motion recognition system based on convolutional neural network and big data

This article focuses on evaluating the efficacy of intelligent image processing techniques using deep learning algorithms in the context of football, to present pragmatic solutions for enhancing the functional strength training of football players. The article commences by delving into the prevailing research landscape concerning image recognition in football. It then embarks on a comprehensive examination of the prevailing landscape in soccer image recognition research. Subsequently, a novel soccer image classification model is meticulously crafted through the fusion of Space-Time Graph Neural Network (STGNN) and Bi-directional Long Short-Term Memory (BiLSTM). The devised model introduces the potency of STGNN to extract spatial features from sequences of images, adeptly harnessing spatial information through judiciously integrated graph convolutional layers. These layers are further bolstered by the infusion of graph attention modules and channel attention modules, working in tandem to amplify salient information within distinct channels. Concurrently, the temporal dimension is adroitly addressed by the incorporation of BiLSTM, effectively capturing the temporal dynamics inherent in image sequences. Rigorous simulation analyses are conducted to gauge the prowess of this model. The empirical outcomes resoundingly affirm the potency of the proposed deep hybrid attention network model in the realm of soccer image processing tasks. In the arena of action recognition and classification, this model emerges as a paragon of performance enhancement. Impressively, the model notched an accuracy of 94.34 %, precision of 92.35 %, recall of 90.44 %, and F1-score of 89.22 %. Further scrutiny of the model's image recognition capabilities unveils its proficiency in extracting comprehensive features and maintaining stable recognition performance when applied to football images. Consequently, the football intelligent image processing model based on deep hybrid attention networks, as formulated within this article, attains high recognition accuracy and demonstrates consistent recognition performance. These findings offer invaluable insights for injury prevention and personalized skill enhancement in the training of football players.

MRI-based Brain Tumor Image Classification Using CNN

Though all brain tumors are not cancerous but they caused a critical disease produced by irrepressible and unusual dividing of cells. For the case of Medical diagnostics of many diseases, the health industry needs help, the current development in the arena of deep learning has assisted to detect diseases. In recent years medical image classification has gained remarkable attention. The most well-known neural network model for image classification problems is the Convolutional Neural Network (CNN). CNN is the frequently employed machine-learning algorithm that is used in Visual learning and Image Recognition research. It is considered to derive features adaptively through convolution, activation, pooling, and fully connected layers. In our paper, we present the convolutional neural network method to determine cancerous and non-cancerous brain tumors. We also used Data Augmentation and Image Processing to classify brain (Magnetic Resonance Imaging (MRI). We used two significant steps in our proposed system. First, different image processing techniques are used to preprocess the images and secondly we classify the preprocessed image using CNN. Brain tumor classification is a process of identifying and separating the cancerous and non-cancerous brain tissues and labeling them automatically. We use the famous machine learning algorithms Convolutional Neural Network which is broadly employed for image classifications. This experiment is conducted on a dataset of 2065 images. In our dataset number of training, examples are 1445, the number of validation examples is 310, and the number of testing example is 310. We also used data augmentation to raise the number of the dataset. We achieved a high testing accuracy of 94.39%. The proposed system displayed sufficient accuracy on the dataset and beat many of the noticeable present methods.

Image Classification Learning Method Incorporating Zero-Sample Learning and Small-Sample Learning

At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems.

Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning

Deep learning has attracted more and more attention in speech recognition, visual recognition and other fields. In the field of image processing, using deep learning method can obtain high recognition rate. In this paper, the convolution neural network is used as the basic model of deep learning. The shortcomings of the model are analyzed, and the DBN is used for the image recognition of diseases and insect pests. In the experiment, firstly, we select 10 kinds of disease and pest leaves and 50000 normal leaves, each of which is used for the comparison of algorithm performance.In the judgment of disease and pest species, the algorithm proposed in this study can identify all kinds of diseases and insect pests to the maximum extent, but the corresponding software (openCV, Access) recognition accuracy will gradually reduce along with the increase of the types of diseases and insect pests. In this study, the algorithm proposed in the identification of diseases and insect pests has been kept at about 45%.

A Systematic Literature Review of Law Enforcement Image Recognition Methods based on Generative Adversarial Networks Framework

Image recognition research based on the Generative Adversarial Networks (GAN) has been widely used in various specialized technical fields because of its universality, adaptability, and scalability of the adversarial framework. The framework achieves the tasks of image generation, synthesis, and transformation to improve the image resolution and recognition rate, which is consistent with the purpose of the police in the image recognition work. However, the task of applying the GAN method to police image recognition is not widely explored. In this paper, we systematically review related research to understand the current applications of GAN in the field of image recognition and summarize their contributions. We analyze 35 academic papers after 2018 to provide a state-of-the-art research stream. According to the GAN applications, we divided the dataset into three domains, which are image-to-image translation, image augmentation, and mixed model. The results show that there is little difference in the number of articles in the three fields. In addition, most of the papers use image conversion methods from more than two domains, which indicates that GAN is flexible in designing the framework according to the research tasks. Based on the methods and challenges in the literature, we further propose future research directions.

Impact of dataset on the study of crop disease image recognition

Datasets are very important in image recognition research based on machine learning methods. In particular, advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models. The quality of datasets directly affects the final effect of these methods. In the research of crop disease image recognition, due to the complication of the agricultural environment and the variety of crops, datasets are scarce at present. Therefore, more and more researches adopt methods based on transfer learning, which can make up for the lack of data in the target domain with the help of other datasets. Among these methods, the selection of auxiliary domain datasets has great impact on the modeling effect of target domain. In order to clarify the impact of datasets on the research of crop disease image recognition, this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning. The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research (IDADP), which have been widely used in recent studies. And the selected deep neural network frameworks include ResNet50, InceptionV3, and EfficientNet. In the method of this study, the datasets are preprocessed first, such as data enhancement. After dividing the auxiliary domain and the target domain, the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset. Finally, the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target. In the experiments, multiple different datasets and different models were tested and compared. The results show that when the test set samples and training sample scenarios are consistent, the recognition accuracy of different network frameworks on multiple test sets is generally high. When the scenarios of test set samples and training samples are inconsistent, the recognition of various test sets by different network models cannot obtain ideal results. For the recognition of crop disease images that are collected from the actual cultivation environment, the use of IDADP dataset modeling is better, and it has more practical value in the actual application of crop disease image recognition. Keywords: crop diseases, datasets, transfer learning, deep learning, image recognition DOI: 10.25165/j.ijabe.20221505.7005 Citation: Yuan Y, Chen L, Ren Y C, Wang S M, Li Y. Impact of dataset on the study for crop disease image recognition. Int J Agric & Biol Eng, 2022; 15(5): 181–186.

Retraction Note to: Analysis of PM2.5 air pollution sources based on image recognition and tracking of sports training process

With the rapid development of social economy and the acceleration of urbanization, air quality is also facing many challenges. Recently, through effective control of pollutant emissions, the number of foggy days and PM2.5 concentration in city T tends to decrease, and air quality has been greatly improved, but haze pollution still occurs occasionally in this area. Analyzing the sources of PM2.5 by industry is particularly important for further improving the air environment quality in T city and implementing targeted industry control. To this end, this study simulated the PM2.5 pollution characteristics of city T in January, April, July, and October 2019 based on the Models-3/CMAQ 3D air quality model and compared and verified the PM2.5 concentration. In practical applications, we will analyze and study the sources of PM2.5 pollution in T cities in the industry based on the “return to zero method” of source analysis based on image recognition and within the framework of a unified model. It is especially important to correctly understand the impact of the target direction on the academic performance of sports training students. The sports field goal setting vector table deeply understands the achievement goal orientation and academic performance of sports training students through literature data methods, questionnaire survey methods, and mathematical statistics methods and provides empirical evidence for teachers to track education. Reference materials are provided by professional students, who manage and develop sports training in a more targeted way in the sports academy. Through the research of image recognition, this paper applies it to the research of PM2.5 air pollution sources and the tracking of sports training process, thereby promoting the better development of air quality and sports training.

Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification

• We propose a joint training methodology for knowledge distillation and open set recognition to improve model robustness. • We demonstrate how knowledge distillation (KD) and open set recognition (OSR) can be performed on 3D point cloud data • The proposed method provides a much smaller model which additionally has enhanced OSR capabilities than the original model. • We find that KD by itself can transfer OSR ability to a student model apart from previously known dark knowledge transfer. • We show how there is a trade-off that comes into play for the student model’s open-set and closed-set performance. Real-world scenarios pose several challenges to deep learning based computer vision techniques despite their tremendous success in research. Deeper models provide better performance, but are challenging to deploy and knowledge distillation allows us to train smaller models with minimal loss in performance. A model also has to deal with open set samples from classes outside the ones it was trained on and should be able to identify them as unknown samples while classifying the known ones correctly. Finally, most existing image recognition research focuses only on using two-dimensional snapshots of the three-dimensional real world objects. In this work, we attempt to bridge these three research fields, which have been developed independently until now, despite being deeply interrelated in practice. We propose a joint knowledge distillation and open set recognition training methodology for three-dimensional object recognition. We demonstrate the effectiveness of the proposed method via various experiments on how it allows us to obtain a much smaller model, which takes a minimal hit in performance while being capable of open set recognition for 3D point cloud data.

A steel classification algorithm based on surface defects

In the research of image recognition and classification, image retrieval, image data mining, and so on, feature extraction is the basic work, among which the texture feature of an image is of great significance to describe the content of the image. In this paper, an extraction method of steel texture features based on a gray co-occurrence matrix is presented, and the influence of various structural parameters on the structure is analyzed. The feature extraction of specific texture images based on a gray level co-occurrence matrix is realized, and the data classification is carried out by using a probabilistic neural network.

Application of Image Recognition Based on Grey Relational Analysis

With the rapid development of society, the requirements and standards of image recognition are getting higher and higher. Therefore, applying some new technologies to image recognition has become an extremely important research topic. In this study, based on the grey relational analysis method, the face image was taken as the main research subject, an image recognition model based on the grey relational analysis method was established, and the related experimental results were obtained. Compared with the traditional face image recognition system, the image recognition method based on the grey relational analysis method had higher recognition speed and good recognition performance. This study provides a new path for the research of image recognition.

Size and Pose Identification of Bolts Based on Monocular Vision

Machine vision has extensively developed in automatic image recognition research, and the automatic identification of bolts can improve the degree of automation greatly for bolts operation. This paper provides a method to extract the outline and the geometrical shape of the bolt thereby to identify the target bolt and locate the position according to the shape and the size of a kind of common six-angle bolt. A dynamic threshold segmentation method is used to extract the bolt geometry information and set the target area as region of interests (ROI). By the sub pixel boundary calculation, Ramer algorithm for segmentation of contours, and Tukey weight function with five iterations for linear fitting, the intersection features which are the bolt corners can be obtained. Through calibration, the relative position of bolt can be located and the size of bolt can be acquired.

Research of Image Recognition of the Feature Expression of Dokkaebi

Research of Image Recognition of the Feature Expression of Dokkaebi

Image segmentation method based on K-mean algorithm

The image is an important way for people to understand the world. How to make the computer have image recognition function is the goal of image recognition research. In image recognition, image segmentation technology is one of the important research directions. This paper uses gray-gradient maximum entropy method to extract features from the image, uses K-mean method to classify the images, and uses average precision (AP) and intersection over union (IU) evaluation methods to evaluate the results. The results show that the method of K-mean can achieve image segmentation very well.

Research of Image Recognition and Classification Based on NIN Model

Focus on the weak feature expression ability of traditional Convolution Neural Network (CNN) in image recognition and classification, the CNN has been improved and optimized by introduction of Mlpconv layer to form a Network in Network (NIN). The NIN enhances feature extraction and abstract expression of the local patch; get better performance of the image recognition. The experiments of weld image recognition and classification show that the optimized CNN can improve the feature expression ability of the whole network by reducing the number of parameters, obtain higher recognition and classification precision, and avoid the fitting of the network model effectively. The CNN achieve improvement in both performance and efficiency.

Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis

With the development of computer vision and various advanced medical imaging devices, medical images contain more and more information. It is difficult to accurately express the characteristics of medical images with a single image feature, and it is not convenient for later diagnosis and recognition processing. In this paper, the chest CT images of children were used as research objects. Its colour features, texture features and shape characteristics were extracted. The main component analysis method is an effective method to reduce the characteristic redundancy. However, considering the limitations of principal component analysis in data standardization, in this research, the method of mean principal component analysis is used to reduce the characteristics of extraction. Experiment result shows that this method achieves a higher contribution rate with fewer dimensions. The conclusions of this research are beneficial to image recognition research.

Structure Design and Image Recognition Research of A Picking Device on the Apple Picking Robot

Picking robot is the inevitable trend of the development of orchard harvester technology in the future. However, automatic apple picking technology is difficult to achieve because of uncertainty in distribution, inconsistent maturity, and uneven quality and other issues. It is hard to locate the position of apples accurately. Therefore, after summarizing the advantages and disadvantages of apple harvesting technology at laboratory and abroad, a picking device was designed. The device is based on the purpose of distinguishing the maturity of apples and consists of shell, cutting device, power device. It can achieve efficient and rapid fruit picking and more accurate differentiation of maturity. For other similar spherical fruit also has some applicability.

Research of Image Recognition and Tracking Algorithm Based on PID Fuzzy Control

Video surveillance system with image recognition and image tracking capabilities has a wide range of applications in the field of city protection, social security and public safety. In this paper, based on the analysis of image detection and tracking principles, image recognition and tracking algorithm based PID fuzzy control is proposed. First, the image on the basis of the gray-scale image is acquired by the video processor and the linearization processing, and then through the expansion operation of mathematical morphology the candidate region of the image recognition is taken, and then the final image region is obtained through the vertical positioning and horizontal positioning of the region Finally, through the PID control algorithm it is the image of the track. Simulation results show that the PID fuzzy control-based image recognition and tracking algorithm possesses good vehicle license plate image recognition and tracking performance and good practicability.

Research of Image Recognition based on Rough Set

Research of Image Recognition based on Rough Set