Recognition Technology Research Articles

Characteristics of Rock Avalanche Deposit in Wangjiapo, Ludian Based on UAV Aerial Image Recognition

Rock avalanche disasters in alpine and gorge regions are frequent and large in scale and cause severe damage. The movement of a rock avalanche is complex and has not been fully studied. The deposits of a rock avalanche can provide valuable insights into its movement process, which is crucial in understanding the rock fragmentation mechanism and predicting disaster-affected areas. Taking the Wangjiapo rock avalanche in Yunnan Province of China as an example, the size, shape and distribution characteristics of the deposit were analyzed based on field surveys, unmanned aerial vehicle (UAV) photography and image recognition technology. Initially, 3062 deposited rock blocks were manually measured in the field. Subsequently, the Particles/Pores and Cracks Analysis System (PCAS) was employed to identify 11,357 rock blocks with an area greater than 0.1 m2 from UAV orthophotos. By comparing the characteristics of the rock blocks obtained through image recognition and manual measurement, the statistical analysis of UAV aerial imagery combined with PACS proved feasible in studying the Wangjiapo rock avalanche. The results showed that the rock block movement was accompanied by fragmentation and sorting processes; furthermore, the roundness increased with the migration distance. Small blocks were more prevalent at the foot of the slope, while irregularly shaped, large blocks dominated in source areas. The movement of huge blocks was characterized by significant potential energy-driven features and inertia advantages, allowing them to travel farther than smaller blocks, and they tended to be concentrated in the central area of the deposit. Additionally, affected by the cementation degree of breccia and the topography, the blocks in the eastern and western deposit areas exhibited different fragmentation and deposition characteristics.

A novel embedded kernel CNN-PCFF algorithm for breast cancer pathological image classification

Early screening of breast cancer through image recognition technology can significantly increase the survival rate of patients. Therefore, breast cancer pathological image is of great significance for medical diagnosis and clinical research. In recent years, numerous deep learning models have been applied to breast cancer image classification, with deep CNN being a typical representative. Due to the use of multi-depth small convolutional kernels in mainstream CNN architectures such as VGG and Inception, the obtained image features often have high dimensionality. Although high dimensionality can bring more fine-grained features, it also increases the computational complexity of subsequent classifiers and may even lead to the curse of dimensionality and overfitting. To address these issues, a novel embedded kernel CNN principal component feature fusion (CNN-PCFF) algorithm is proposed. The constructed kernel function is embedded in the principal component analysis to form the multi-kernel principal component. Multi-kernel principal component analysis is used to fuse the high dimensional features obtained from the convolution base into some representative comprehensive variables, which are called kernel principal components, so as to achieve the purpose of dimensionality reduction. Any type of classifier can be added based on multi-kernel principal components. Through experimental analysis on two public breast cancer image datasets, the results show that the proposed algorithm can improve the performance of the current mainstream CNN architecture and subsequent classifiers. Therefore, the proposed algorithm in this paper is an effective tool for the classification of breast cancer pathological images.

Speaker Recognition System Using Hybrid of MFCC and RCNN with HCO Algorithm Optimization

Though there are advancements in speaker recognition technology, available systems often fail to correctly recognize speakers especially in noisy environments. The use of Mel-frequency cepstral coefficients (MFCC) has been improved using Convolutional Neural Networks (CNN) yet difficulties in achieving high accuracies still exists. Hybrid algorithms combining MFCC and Region-based Convolutional Neural Networks (RCNN) have been found to be promising. In this research features from speech signals were extracted for speaker recognition, to denoise the signals, design and develop a DFT-based denoising system using spectrum subtraction and to develop a speaker recognition method for the Verbatim Transcription using MFCC. The DFT was used to transform the sampled audio signal waveform into a frequency-domain signal. RCNN was used to model the characteristics of speakers based on their voice samples, and to classify them into different categories or identities. The novelty of the research was that it used MFCC integrated with RCNN and optimized with Host-Cuckoo Optimization (HCO) algorithm. HCO algorithm is capable of further weight optimization through the process of generating fit cuckoos for best weights. It also captured the temporal dependencies and long-term information. The system was tested and validated on audio recordings from different personalities from the National Assembly of Kenya. The results were compared with the actual identity of the speakers to confirm accuracy. The performance of the proposed approach was compared with two other existing speaker recognition the traditional approaches being MFCC-CNN and Linear Predictive Coefficients (LPC)-CNN. The comparison was based the Equal Error Rate (EER), False Rejection Rate (FRR), False Match Rate (FMR), and True Match Rate (TMR). Results show that the proposed algorithm outperformed the others in maintaining a lowest EER, FMR, FRR and highest TMR.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators

BackgroundDebriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants.MethodsWe used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns.ResultsTen debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%.ConclusionOur results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.

Coal-gangue sound recognition using hybrid multi-branch CNN based on attention mechanism fusion in noisy environments

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

High-precision identification of highly similar Pinelliae Rhizoma and adulterated Rhizoma pinelliae pedatisectae through deep neural networks based on vision transformers.

Pinelliae Rhizoma is a key ingredient in botanical supplements and is often adulterated by Rhizoma Pinelliae Pedatisectae, which is similar in appearance but less expensive. Accurate identification of these materials is crucial for both scientific and commercial purposes. Traditional morphological identification relies heavily on expert experience and is subjective, while chemical analysis and molecular biological identification are typically time consuming and labor intensive. This study aims to employ a simpler, faster, and non-invasive image recognition technique to distinguish between these two highly similar plant materials. In the realm of image recognition, we aimed to utilize the vision transformer (ViT) algorithm, a cutting-edge image recognition technology, to differentiate these materials. All samples were verified using DNA molecular identification before image analysis. The result demonstrates that the ViT algorithm achieves a classification accuracy exceeding 94%, significantly outperforming the convolutional neural network model's 60%-70% accuracy. This highlights the efficiency of this technology in identifying plant materials with similar appearances. This study marks the pioneer work of the ViT algorithm to such a challenging task, showcasing its potential for precise botanical material identification and setting the stage for future advancements in the field.

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Retraction Note: Application of intelligent trajectory analysis based on new spectral imaging technology in basketball match motion recognition

Handwritten Digit Recognition by Deep Learning

This paper evaluates recent advances in handwritten digit recognition models, focusing on strategies developed and deployed in practical applications. The project utilizes both traditional and deep learning approaches, employing architectures such as Convolutional Neural Networks (CNNs). This paper explores the comparative performance of various models, discusses their deployment in real-world scenarios, and highlights future prospects for enhancing handwritten digit recognition technology. Keywords: Handwritten Digit Recognition, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), AlexNet, Deep Learning for Image Classification, Neural Networks, Image Preprocessing, Data Augmentation, Feature Extraction, Image to Grayscale Conversion, Image Normalization, Training and Validation, Accuracy Metrics, Model Evaluation, Transfer Learning, Classification Algorithms, Real- Time Processing, Computer Vision, Image Recognition.

Computer vision in situ

Background: This article shows a visually impaired person (VIP) trying to locate products while shopping using the commercially available computer vision device Orcam. Method: Based on ethnomethodological conversation analysis and perspectives on materiality and agency from a post-praxiological position, the study shows, through detailed analysis of transcribed video excerpts, the observable sense-making practices related to this technology. Results: The study shows the VIP and researcher-participants exploring what the device can and cannot do, and how the local body–object–environment relations need to be organized to make the device scan. The study shows the value of applying a post-praxiological approach to understanding socio-material practices. It also introduces the ‘video-based contextual inquiry’ method as a form of researcher engagement in producing the situation and the data collection. Discussion/conclusion: The article provides two novel contributions: (1) to the field of ethnomethodology and conversation analysis research, with a critical reflection on semi-experimental data collection and the role of the researcher, the materials, and the distribution of agency; and (2) to the field of impairment and disability studies, with insights on locally organized body–object–environment relations and the design of artifacts for computer vision recognition technology.

An Enhanced LBPH Algorithm for Robust Face Feature Extraction

From security systems to human-computer interaction, face recognition technology is a key component in many different applications. In this domain, the local binary pattern histogram (lbph) algorithm has shown great promise due to its ability in texture-based feature extraction and robustness. In this study, the performance of lbph algorithm is improved by optimising important parameters: radius, number of neighbours and grid configuration. Tweaking these parameters systematically enable us to get the most success out of this algorithm. Lbph algorithm parameters has been tuned into a 5x7 dimensional matrix, which gives us total of 35 grids with equal width and height pixels. In other words, one central pixel plus 34 neighbouring pixels where the radii of 3 square neighbourhoods can be adjusted. The study combines the experimental exploration with fine-tuning via machine learning approaches to optimize lbph algorithm. Finally, we have provided experimental results on intra-class and inter-class feature distribution analysis conducted on selected images taken under constraints and unconstraint environments. The performance of our novel 34N-LBPH algorithm showed very low values by obtaining intra-class average means of 0.031, 0.078, and 0.101 for three classes under constraint environment indicating the proposed 34n-lbph is robust for facial feature extraction. The findings suggest that our enhanced algorithm, 34 neighbour linear binary pattern Histogram (34N-LBPH) can effectively handle variations in lighting, expressions and occlusions, contributing to the advancement of facial feature extraction for face recognition.

Drowsy Driver Warn System Using OpenCV

The "Drowsy Driver Alarm" project leverages cutting-edge machine learning and face recognition technologies to address the critical issue of driver drowsiness on the road. Drowsy driving is a major cause of accidents and fatalities worldwide, highlighting the urgent need for effective countermeasures. Our research focuses on developing an intelligent system capable of detecting signs of drowsiness in real-time, providing timely warnings, and ultimately preventing accidents.

HDN-Net: A Hybrid Deep Neural Network to Improve Iris Recognition in Unconstrained Environments with Eyeglasses

Recently, the use of iris recognition technology for biometric authentication has gained widespread acceptance due to the rich texture of the iris region, which provides a reliable standard for recognising individuals, as well as the non-intrusive nature of this method. However, the presence of eyeglasses poses a significant challenge to the accuracy of such systems. In unrestricted environments, current iris recognition techniques cannot effectively extract distinguishing features of the iris. Eyeglasses introduce scratches, specular reflections, dirt, blurriness, and other noise factors over the image of the iris, resulting in low recognition accuracy. To tackle this challenge, researchers have proposed the HDN-Net architecture. This architecture employs a multi-CNN model to combine the features of both the right and left iris images, extracting more distinguishing features to improve the accuracy of the classification task in the presence of challenges caused by eyeglasses. Experiment results show that the proposed iris recognition system achieves more promising performance compared to previous methods used in this field. The overall performance of our suggested HDN-Net method on the UBIRIS.V2 and CASIA-Iris.V4-1000 databases achieved 97.89% and 98.79% accuracy, respectively. Thus, the proposed HDN-Net method consistently outperforms other traditional and deep learning approaches and has the possibility to improve the accuracy of iris recognition systems (IRS) in real-world scenarios.

Evaluative Performance Program E-Tilang di Simpang Raya Kota Surabaya

ETLE (Electronic Traffic Law Enforcement) is a set of electronic devices that use Automatic License Plate Recognition (ANPR) technology in the form of cameras that automatically detect motorized vehicles and cars with traffic violations E-TLE system, which is part of law enforcement efforts, this system aims to reduce problems caused by traffic violations, such as congestion and accidents, and ensure order on the highway. The e-Tilang system was pioneered by the East Java Regional Police, especially the Surabaya Police, which has this innovation as a crucial role in improving safety and regularity of electronically based traffic, the implementation of ETLE tickets in Surabaya City has increased in 2022 and 2023 compared to 2021. Which in 2021 the ETLE program was intensified due to the Covid-19 pandemic in order to increase the efficiency of technology-based services. The ETLE camera system uses CCTV cameras that are monitored directly by Police Officers with a total of 22 monitoring camera points in several parts of Surabaya City. where each officer oversees 4 areas with 8 CCTVs on the monitoring screen. Although it has had a positive impact on the implementation of ticketing, there are many things that should be evaluated from the process of running E-TLE at Surabaya Police Station. Mainly in the process of implementing and providing maximum fines, as well as the limited number of employees and time in ETLE supervision. So that the Process on Performance and the Facilities used should be evaluated in the running of the ETLE system in Surabaya City. The method used in this research is descriptive qualitative. The focus of this research is to find out the results of the Evaluation of the E-Ticket Policy Program (E-TLE) at the Surabaya City Road Intersection The theory used in the research is Gupta & Jana's Evaluative performance theory. There are 4 indicators from this theory, namely total costs and fines, quality of planning and control, quality of decision making and system characteristics. The data collection technique in this study is by conducting interviews, observation and documentation. In this research, from these 4 indicators, some are quite appropriate, but there are still many shortcomings that should be evaluated from the system and the quality of performance of the Surabaya police litas unit. The performance evaluation of the E-Tilang program in Surabaya shows success in managing total costs and fines, the quality of planning and control, and the quality of decision making. This program includes the use of 40 CCTV camera units and fiber optic networks that support the effectiveness of law enforcement, as well as the management of fair fines. Careful planning and better coordination after the evaluation ensured efficiency in recording violations using gadgets and the E-Tilang application.

APPLICATION OF MACHINE LEARNING TECHNIQUES TO INCREASE THE LEVEL OF ACCURACY OF OPTICAL CHARACTER RECOGNITION RESULTS

One of the most pervasive processes of modernity is undoubtedly digitalization, which has encompassed all key spheres of human life. The development of information technology has contributed to large-scale changes not only in the everyday aspect of life, but also more globally, automating complex business processes in the field of entrepreneurship, economics, and healthcare. The transition to digital data and documentation has provided greater accessibility to necessary information and has also enhanced the efficiency of its analysis and processing. Due to this fact, optical character recognition (OCR) technology has gained significant importance, enabling the identification and extraction of textual data from images. OCR systems play a pivotal role in the digital transformation of society as they eliminate the need for manual handling of textual information in images and are applicable in automating the majority of business processes associated with paper-based data processing, such as gathering statistical data from paper forms, reflecting paper documents in electronic document management systems, converting textual information into audio files, and so on. This paper is dedicated to describing optical character recognition technology, as well as providing an overview of machine learning techniques that are actively used in the context of its modern implementation, in order to enhance the quality of the obtained results. In addition, the paper presents the principles of operation of the described approaches, their capabilities, as well as some limitations that may be encountered when using them in various scenarios.

Application of low energy building thermal energy optimization based on image recognition and light sensors in urban green environment planning

The paper analyzes the application of thermal energy optimization scheme based on image recognition and light sensor technology in low-energy buildings, in order to improve building energy efficiency, reduce energy consumption and provide scientific basis for urban green environment planning. In this paper, image recognition technology is used to monitor the light change in the external environment of the building, and the indoor light intensity data is obtained by the light sensor in real time. Combined with big data analysis, building heat optimization model is established, intelligent control system is designed, and the heating and cooling system of the building is dynamically adjusted, so as to achieve efficient use of heat energy. The experimental results show that the thermal energy optimization method based on image recognition and light sensor has a good application prospect in low-energy buildings, which not only effectively reduces energy consumption, but also contributes to the sustainable development of urban green environment.

Diversity-driven Privacy Protection Masks Against Unauthorized Face Recognition

Face recognition (FR) technologies have enabled many life-enriching applications but have also opened doors for potential misuse. Governments, private companies, or even individuals can scrape the web, collect facial images, and build a face database to fuel the FR system to identify human faces without their consent. This paper introduces PMask to combat such a privacy threat against unauthorized FR. It provides a holistic approach to enable privacy-preserving sharing of facial images. PMask preprocesses the facial image and hides its unique facial signature through iterative optimization with dual goals: (i) minimizing the amount of noise to ensure high image quality and (ii) minimizing the perception loss between the privacy-protected face and the original face to ensure the face is recognizable to be the same person by humans. Extensive experiments are conducted on eight representative FR models to evaluate PMask against unauthorized FR. The results validate that PMask provides much stronger protection, introduces less perceptible changes to facial images, and runs faster than state-of-the-art methods to provide privacy protection with a better user experience.

Research on intelligent control and emergency response system for power cable tunnels based on AI image recognition and ultra-wideband technology

Research on intelligent control and emergency response system for power cable tunnels based on AI image recognition and ultra-wideband technology

Multi-attribute balanced dataset generation framework AutoSyn and KinFace Channel-Spatial Feature Extractor for kinship recognition

In the field of kinship verification, facial recognition technology is becoming increasingly vital due to privacy concerns, ethical disputes, and the high costs associated with DNA testing. We have developed a novel method, the AutoSyn framework, to synthesize facial images and enhance kinship image datasets, effectively addressing the challenges of scale and quality in existing datasets. By employing a strategy that mixes ages and genders in the synthesized images, we minimize the impact of these factors on kinship recognition tasks. We have enhanced the original KinFaceW-I dataset by integrating ten distinct styles, including diverse combinations of gender, ethnicity, and age. This enrichment significantly improves both the quality and quantity of the images. Furthermore, this paper introduces an efficient feature extractor for kinship tasks, KinFace-CSFE, within a siamese neural network framework. This model not only utilizes meticulously designed channel feature extraction but also incorporates mixed kernel size spatial attention mechanisms to better focus on local features. We have also integrated YOCO data augmentation techniques to simulate complex imaging conditions, enhancing the model’s robustness and accuracy. The effectiveness of these innovations has been validated through experiments on the KinFaceW-I, KinFaceW-II, and synthesized Syn-KinFaceW-I datasets, achieving accuracy rates of 82.7%, 94.1%, and 83.2% respectively. These results significantly surpass both traditional models and current advanced models.