Image Recognition Applications Research Articles

The effect of mobile image recognition-based augmented reality application on students’ acquisition of science processing skills and motivations to learn science

ABSTRACT With the development of mobile phone applications with innovative features, the application of mobile recognition technology, such as a mobile image recognition-based (AR) application, is becoming increasingly widespread. Mobile Image Recognition-Based AR connect nearby objects or other physical targets with digital information. The purpose of this study is to investigate the effects of using an image recognition-based AR application on students’ acquisition of science process skills and their motivation toward science learning. The study involved 56 students enrolled in two sections of tenth-grade female students in a private school in Jordan. Each section was randomly assigned to either the control group or the experimental group. The experimental group studied a unit from the biology textbook with image-based recognition content. In contrast, the control group studied the same unit using the conventional teaching method based on the traditional textbook content. The researchers administered a science both groups a science process skills test and a motivation toward learning science scale. Results showed that participants who used a mobile image recognition application to learn science had higher acquisition of all science skills and a higher motivation toward learning science than those who used the conventional learning method.

Supervised Learning: An InDepth Analysis

Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also highlighted. Challenges include overfitting data quality, interpretability. Scalability discussed emphasizing areas for ongoing research. Future directions point towards transfer learning. Integration with semisupervised and unsupervised methods. Explainable AI and quantum machine learning promise further enhancements. They will impact supervised learning across various domains. Key words: Supervised learning, Machine learning, Labeled datasets, Algorithm training, Outcome prediction, Data classification, Input-output pairs, Model generalization, Accurate predictions, Real-world applications, Spam filters, Recommendation systems, Medical diagnosis, Autonomous driving, Efficiency enhancement, Decision-making, User experience, Data handling.

Malaria cell identification using improved machine learning and modified deep learning architecture

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Automatic Detection of Banana Maturity—Application of Image Recognition in Agricultural Production

With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification.

Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching

INTRODUCTION: The paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education.

CutFreq: Cut-and-Swap Frequency Components for Low-Level Vision Augmentation

Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.

Neuromorphic Vision Sensor driven by Ferroelectric HfAlO

The rapid progress in bioinspired machine vision supported by intelligent computing has attracted attention of the modern electronics due to their wide applications in image recognition, artificial intelligence, and medical applications. The ferroelectric materials are considered as most reliable for the fabrication of these devices due to their nonvolatile and dense memory capacity as well as stability. Here, we report the neuromorphic vision sensor (NVS) based on a self-grown ferroelectric interfacial HfAlO layer. The unique properties of graphene (Gr) and silicon are integrated through the ferroelectric layer to fabricate the NVS, which can mimic the human eye system. The sensor efficiently demonstrates the basic neural functions including pair-pulsed facilitation, depression, inhibition, and excitation under electric as well as optical stimuli. The optical spike rate (2 Hz–10 Hz) as well as the amplitude (0.5 μW/cm2-1.5 μW/cm2) dependent plasticity, exhibited excellent facilitation and depression index of 1.62% and −1.86% with the minimum energy consumption of only 3.5 fJ per spike. The high endurance of 1 × 107 cycles confirm its reliability and data security. Our device with excellent features of optical signal detection, information processing, and data storage with its simple structure can be efficiently utilized as an image sensor for robotics and intelligent machine vision systems.

A comparison of deep learning-based object detection for unmanned aerial vehicle

Unmanned aerial vehicle (UAV) plays a critical role in the field of object detection, and machine learning techniques have significantly advanced the field in recent years. This paper provides a comprehensive overview of machine learning developments for UAV object detection. The process involves multiple steps. Firstly, deep learning and Convolutional Neural Network (CNN) are widely utilized to extract precise features and train object detection models for accurate classification and efficient recognition of target objects in images and videos. Secondly, classical object detection algorithms such as Faster R-CNN, You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), have been enhanced to improve accuracy and real-time performance. In this review, we primarily focus on comparing the principles of CNN and YOLOv5 themselves, as well as their applications in object detection and image recognition. Ultimately, it becomes apparent that CNN is better suited for processing image data, automatically extracting features, and achieving more accurate classification and detection. On the other hand, YOLOv5 directly performs detection on large images, significantly reducing computation time compared to CNN.

The Application and Development of Image Recognition Technology in the Field of Computer Vision

Image recognition is an important field of computer vision research. With the emergence of convolutional neural networks, image recognition has rapidly developed in many fields and has entered people's daily lives. This article reviews image recognition applications and future prospects in three representative fields: healthcare, intelligent driving, and security monitoring. In medical image analysis, image recognition technology can improve medical image interpretation speed and accuracy by automatically learning image features, providing doctors with more comprehensive information and decision-making support. Regarding intelligent driving, image recognition can help vehicles perceive and understand the traffic environment in real time, improving driving safety and automation level. In terms of security monitoring, image recognition plays a vital role in facial recognition, object detection, and behavior analysis, providing intelligent monitoring and security guarantees. However, these areas still have challenges, such as large data volumes, labeling difficulties, and privacy issues. In the future, people can look forward to combining image recognition technology with other technologies to improve models' interpretability and decision-making ability and further promote the development of these fields.

Performance of convolutional neural networks on CIFAR and Dog Breed classification dataset

This research aims to study the deep learning applications in image recognition and classification tasks. The advantages and limitations of deep learning are explored by analysing existing deep learning algorithms and their applications on tasks such as using images to do classification, image segmentation, and target detection. In the experimental part, this paper evaluates the performance of deep learning in image recognition by using classical neural network models, including training and testing models on a large size of image datasets. The results show that neural network has good classification and detection capabilities in image recognition tasks and also achieves good results for image segmentation tasks. However, the model training process of shallow neural network models is time-consuming and performs poorly for small-scale datasets. Based on these situations, this paper proposes some optimization strategies to achieve high performance and efficiency of neural networks in image recognition and analyze the performance differences between different strategies .

Content Based Image Retrieval System Using CNN based Deep Learning Models

Digital information search archives on the internet and other platforms have been an essential part of our daily lives. With the use of huge amounts of digital images, it is paramount to develop efficient solutions to retrieve images seamlessly. Due to inadequate textual description, the task of automated image retrieval is quite complicated. Therefore, retrieval of images by analysing their visual content is an exciting task and research-worthy. Text based Image Search (TIBR) is time consuming as initially, every image has to be linked with a keyword or text to be extracted on query search. In the proposed system, an efficient algorithm for Content Based Image Retrieval (CBIR) using pre-trained CNN-based Deep Learning models to extract deep features of an image has been developed, which significantly increases the performance of the image retrieval process. Convolutional Neural Networks (CNN) has evolved as an efficient deep learning solution for the CBIR systems that work in image recognition applications. The results of retrieval are evaluated in terms of precision performance parameters. Emphasis has been given to the comparison of the efficiency of the system using two different pre-trained CNN-based Deep Learning models based on VGG16 and ResNet-50 architecture. Transfer learning enables us to develop robust and efficient working systems using pre-trained models. In the proposed system, pre-trained models have been used to derive features using a deep learning CNN network trained for a large image classification dataset ImageNet. This approach outperforms many contemporary CBIR systems. As most CBIR systems depend on query images and as most users of such systems are non-professional, we have developed a user interface for the built prototype systems. The efficiency of the CBIR systems with pre-trained models VGG16, and ResNet-50 are compared for better image retrieval and reliability. The proposed system has addressed a few existing problems in CBIR system design and developed a suitable solution with less semantic gap and better efficiency in image retrieval.

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

Salt-assisted vapor–liquid–solid growth of high-quality ultrathin nickel oxide flakes for artificial synapses in image recognition applications

Salt-assisted vapor–liquid–solid growth of high-quality ultrathin nickel oxide flakes for artificial synapses in image recognition applications

High performance resistive random access memory based on Ag/TiO2 Nanorods/FTO for image recognition applications

This study investigates the electrical and synaptic properties of Ag/TiO2 nanorod/FTO-based resistive random access memory (RRAM) devices, focusing on the impact of different seed layer thicknesses on nanorod thickness and RRAM performance. The devices exhibit notable achievements, including a high DC endurance of up to 150,000 cycles, a self-compliance function of 10−2 A, and a resistance switching ratio exceeding 100-fold. Notably, the device with a 3.5 μm TiO2 nanorod thickness demonstrates exceptional stability. Analysis of the IV curve reveals that the switching mechanism is attributed to space-charge-limited conduction (SCLC) resulting from electron trapping in oxygen vacancy traps. Furthermore, the device maintains stable synaptic properties even after undergoing 20 cycles of long-term potentiation and depression. When employed in the context of artificial neural networks for image recognition, these devices attain high accuracy rates in recognizing handwritten digits, achieving approximately 90% accuracy for 8 × 8 pixel images and approximately 88% for 28 × 28 pixel images, demonstrating their suitability for practical applications.

Sequential architecture-agnostic black-box attack design and analysis

Although adversarial machine learning attacks on image recognition models have been heavily investigated, the rising popularity of vision transformers revitalized the research on this topic. Due to the fundamental architectural differences between CNNs, which still dominate the image recognition applications, and transformers, the state-of-the-art attacks designed for CNNs are not effective against transformers, and vice versa. Such lack of transferability in attacks and the growing architectural heterogeneity in practice make the black-box attack design increasingly challenging. However, skillful attackers can handle the increasing uncertainty in target model architecture following two main approaches: designing transferable attacks that are robust to the architectural uncertainty in target model, and identifying the target architecture for attack selection. In this work, following the latter approach we propose a novel architecture-agnostic black-box attack design and analyze its performance. Experiments show that the proposed method, with a reasonable query overhead, outperforms the recent robust attack designs that follow the former approach. Different from the existing methods, the proposed method optimizes a trade-off between prior information about the target model and number of queries.

Spectrum changes of pulsed chirped Generalized Hermite cosh-Gaussian beam through turbulent biological tissues

The main objective of this study is to investigate the development of the spectral intensity profile of a pulsed chirped Generalized Hermite cosh Gaussian (GHCG) beam through turbulent biological tissues. This is achieved using Schmitt's model for the power spectrum refractive index, the extended Huygens-Fresnel integral, and the Fourier Transform method. Graphical representations need to be generated to explore the effects of beam parameters and biological turbulence parameters on the considered beam. Furthermore, a thorough discussion is conducted to examine the influence of both optical parameters and biological tissue characteristics on spectral shifts at various observation positions. The findings hold potential applications in image recognition, medical devices, and the noninvasive optical diagnosis of biological tissue.

Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures

Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an objective (loss function). CNN is finding various applications in image recognition, semantic segmentation, object detection, and localization. The present work deals with the prediction of the welding efficiency of the Friction Stir Welded joints on the basis of microstructure images by carrying out training on 3000 microstructure images and further testing on 300 microstructure images. The obtained results showed an accuracy of 80 % on the validation dataset.

Space heating performance analysis of air source heat pump integrated with image gray recognition-based defrosting controller

In practice, owing to the unreasonable defrosting initiating method of air source heat pumps (ASHPs), mal–defrost occurs frequently, further leading to the increase of building heating energy consumption. In recent years, with the development of image recognition technology in many fields, it has been expected to be used in defrosting initiating control methods of ASHPs. Although former investigations demonstrate that it is feasible and promising, there is still lack of a control strategy to make it become reality. To promote the application of image recognition in defrosting initiating method of ASHPs, a control strategy for the defrosting initiating method based on image gray recognition was proposed in the present work and used in an image gray recognition equipment (IGRE). Then, nine groups of experiments were conducted based on this new IGRE method. Results indicate that the proposed control strategy for the IGRE method is feasible and practical. By employing this new IGRE method, the defrosting accuracy rate and the average COP of the ASHPs are respectively 42.86% and 36.60% higher, compared with those using the conventional T–T method. In complex and variable environments, the defrosting accuracy rate of this new IGRE method reaches 93.33% in a 10–day test.

Retracted: Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Retracted: Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines