Recognition Technology Research Articles

A Review on Radar-Based Human Detection Techniques.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

Exploring the effects of multiple online interaction on emotions of L2 learners in synchronous online classes

The decline in both the quantity and quality of interaction has emerged as a notable challenge in online learning. However, the definition of interaction quality remains unclear. This study clarifies it as a decrease in the breadth of interaction, which refers to interaction that can only cover a smaller number of learners. To address this, a synchronous interaction modality, termed Multiple Online Interaction (MOI), based on Zoom's interactive tools, was introduced. In a quasi-experiment involving 58 Chinese L2 learners (with 30 beginner and 28 intermediate students), emotions were assessed using the Brief Mood Introspection Scale (BMIS) while real-time emotional dynamics were revealed through the analysis of 5129 facial expression images during a 35-min synchronous class. MOI participants reported higher levels of Lively and Happy but also experienced more Nervous and less Calm. These emotional dynamics, tracked through expression recognition technology, demonstrate that MOI's impact is primarily observed during the first Grammar & Practice section of the teaching. The empirical findings of this study provide practical insights for educators aiming to conduct effective online teaching in the future.

Crop Pest Detection using Convolutional Neural Network

Pests in plants can cause significant losses in agricultural production. As a result, various technologies are used nowadays to improve agriculture's efficiency and make it more sustainable. This research highlights the contribution of machine learning algorithms and image recognition technologies for pest identification. Farmers can use the system to recognize pests and take the necessary actions to reduce them. Convolutional Neural Networks (CNN) is used in this study for image recognition tasks, including pest identification in agricultural fields. The algorithm is trained using the Agricultural Pests Dataset acquired from Kaggle. The experiment results showed that the CNN performed better than the other state-of-the-art machine learning models, with a much lower false rejection rate of 0.12% and an accuracy of 99%.

Snn and sound: a comprehensive review of spiking neural networks in sound.

The rapid advancement of AI and machine learning has significantly enhanced sound and acoustic recognition technologies, moving beyond traditional models to more sophisticated neural network-based methods. Among these, Spiking Neural Networks (SNNs) are particularly noteworthy. SNNs mimic biological neurons and operate on principles similar to the human brain, using analog computing mechanisms. This capability allows for efficient sound processing with low power consumption and minimal latency, ideal for real-time applications in embedded systems. This paper reviews recent developments in SNNs for sound recognition, underscoring their potential to overcome the limitations of digital computing and suggesting directions for future research. The unique attributes of SNNs could lead to breakthroughs in mimicking human auditory processing more closely.

Apple recognition in complex environments based on FC-DETR

This study aims to address the challenges of apple recognition in complex environments by proposing a solution based on the FC-DETR model. Apple cultivation is an important part of agriculture in Xinjiang, but the increasing shortage of labor has driven the demand for automated harvesting technologies. Therefore, research and development of apple recognition technology in complex environments have become crucial. While CNN architecture models can accurately identify apples in conventional settings, their inherent receptive field limitations prevent them from fully capturing global features in noisy and complex environments, making it difficult to achieve the accuracy and robustness required for practical applications. To solve this problem, this study proposes a real-time object detection model, FC-DETR. This model incorporates the innovative FEMA-BasicBlock residual module, the CAFM cross-scale adaptive feature fusion module, and the novel Inner-WIoU loss function, thereby enhancing feature processing, multi-scale feature selection and integration, and detection accuracy. Experimental results show that the FC-DETR model achieves an apple recognition accuracy of 87 % and a recall rate of 82 % in complex backgrounds while maintaining a lightweight design. This study not only makes significant advances in automated apple harvesting technology but also contributes to improving the efficiency and sustainability of the apple industry in Xinjiang and globally.

Application of Image Recognition Technology Using Deep Learning to the Medical and Agricultural Fields

Application of Image Recognition Technology Using Deep Learning to the Medical and Agricultural Fields

Barcode-less Fruits Classification Using Deep Learning

<p>Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.</p>

Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.

The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate. • The facial gestalt of WBS, often described as "elfin," includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS. • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.

Quarantine Order Violators System using Face Recognition (FACID)

Face recognition technology is commonly utilized for user authentication and verification by analysing a digital image of a person's face and matching it against a database of faces for identification purposes. The COVID-19 pandemic has led to mandatory quarantines and the use of quarantine bracelets for some individuals, which can be time-consuming and require a lot of effort. Face recognition technology can help raise awareness about the current situation and alleviate some of the burden associated with quarantine measures. The aim of this research is to concentrate on the face recognition of individuals with a history of quarantine, as a measure to prevent the spread of COVID-19 in educational institutions like universities, colleges, and schools. This research study concentrates on individuals with a history of quarantine orders. The system will employ the Histograms of Oriented Gradients (HOG) algorithm for face detection. Additionally, the system will utilize the Face Landmark Algorithm to compare the 128-d vector with images stored locally. The system will make use of the Helen face collection dataset for its data requirements. The aim of this study is to explore and identify techniques for detecting individuals who violate quarantine measures and issuing notifications through the proposed system. By implementing this system, it could contribute to creating a more secure environment within the educational institution and potentially reduce the spread of the virus.

Application of sound and vibration recognition technology based on optimized features in underground cable external breakage prevention

Application of sound and vibration recognition technology based on optimized features in underground cable external breakage prevention

Attention Retinex Network(A4R-Net) for face detection under low-light environment

The degradation of recognition rates in low-light environments is a critical issue in terms of security when using object and face recognition technologies in various locations. Existing low-light enhancement models have shown limitations in terms of computational cost and performance. However, this paper overcomes these limitations. The experimental results demonstrate that our model achieves the same performance as existing models with 13 times lower computational cost and a face detection performance of 82.2%.The structure of this paper is as follows: Introduction, which provides the background and explains the limitations of existing models. Proposed Method, which details the structure and working principles of A4R-Net. Experimental Results, which present the evaluation of low-light enhancement performance and the comparison of face detection using YOLOv4 [1]. Conclusion, which discusses the contributions of this research and future research directions.The source code and dataset is https://github.com/Obiru2698/obiru2698.github.io/

Real-Time Human Profiling: Unveiling Age, Gender and Emotions Using Deep Learning

This paper introduces the development of a real-time system, which deploys an integrated use of Flask, deep learning, Convolutional Neural Networks (CNNs) and cascade classifier approach to detect age, gender, and emotion from facial images. While its numerous applications—ranging from marketing and medical services to security surveillance—immediately catches the eye, the facial recognition technology is fast becoming the hot topic. Our suggested system aims at absolutely determining the age, gender or emotional state of a person instantaneously in real life. Flask, Python micro web framework, is at the basis of the software, providing the desired functionalities for the exchange of information between the processing engine and the front end. Convolutional Neural Networks (CNNs) which are a part of deep learning for tasks involving feature extraction and classification are the main tool used by most learning algorithms. CNNs are widely used in face recognition systems mainly due to the fact that they perform very well at such tasks as image processing. On the one hand, the model uses cascade classifiers for superior face detection, thereby finding and separating face regions in input images or video streams. Unlike some of the approaches that require heavy computation, these classifiers are computationally light solutions that can run in real-time even on resource- limited devices. The system capacity to identify age, gender, and emotion accurately in real-time is exemplified through: performance evaluation. We have used various means to stress the system and ensure that it is precise and offers timely results over the given period. By means of varied testing stages, we have discovered that the system usually brings high levels of precision and validity for the numerous data sets and trial situations. In this regard, be it differentiating between individuals’ age or readily identifying the exact gender or recognizing nuanced emotional clues, the system performs at the optimum level.

Detection of Encroachment in Civil Structure Using Dynamic Learning Techniques

Identifying cracks through visual inspection and automated surveys are both effective methods. While both approaches yield satisfactory distress analysis results, automated crack recognition technology stands out for its speed and cost-effectiveness compared to traditional human visual detection methods. This study introduces an innovative approach, namely "crack encroachment in concrete structures classified using DCNN" which employs a Deep Convolutional Neural Network. To enhance the precision of crack detection, the input image undergoes denoising through ADNLMF (Anisotropic Diffusion Non-Local Mean Filtering), preserving edges, textures, and features. Crack discrimination between crack and non-crack images was achieved using Yolo v3, and a deep convolutional neural network classifier was employed to identify specific crack types based on their widths, utilizing crack width transform. This method not only enhances accuracy but also reduces network complexity and processing time. A comprehensive performance comparison with existing crack identification techniques indicates that our proposed methodology for concrete structure crack recognition produces superior results.

Using AI-Powered Speech Recognition Technology to Improve English Pronunciation and Speaking Skills

Using AI-Powered Speech Recognition Technology to Improve English Pronunciation and Speaking Skills

The relationship between emotions and HRV under the stimulation of bra images

It is a stressful ordeal in general to buy bras online aimlessly. The wise approach for eCommerce platform is to use emotion recognition to effectively assess consumers’ needs through the bra images, which can quickly close their preferences and accurately recommend them suitable bras. However, the significant physiological features that can be used for emotion recognition in fashion are still in the exploratory stage. This study focuses on the relationship between cardiac activity and emotional responses (the different levels of Satisfaction (S) and Attractiveness (A) under the stimulation of bra images. Firstly, we labelled 96 bra images in the dimensions and selected 45 bra images to make three groups of emotional stimuli using k-means clustering analysis. Then, we collected Photoplethysmogram (PPG) signals from 46 subjects consisting of 11 males and 35 females simulated by three groups of the stimuli, extracted 10 metrics of heart rate variability (HRV), and then analyzed the relationships between HRV and emotions based on their subjective evaluation. The males and females have different cardiac activity patterns under the same emotion, that the parasympathetic of males are more active than females. In a non-neutral emotion, the parasympathetic nervous system would keep an activated state. However, it is dominated by the parasympathetic nervous system whether in high or low satisfaction. While high and low attractiveness are respectively dominated by the sympathetic and parasympathetic nerves. The results of this study have certain significance for understanding the physiological phenomenon of emotions and further help to explore emotion recognition technology which can be used in product development to enhance the competitiveness of enterprises.

Extracting dashcam telemetry data for predicting energy use of electric vehicles

Prior to the acquisition of an electric vehicle, pre-evaluation of vehicle energy use is desirable to assess whether the intrinsic vehicle electrical storage capability is satisfactory. However, inconsistency in general vehicle modelling may provide unreliable predictions concerning energy usage. To increase the prediction reliability, the use of route-specific driving cycle data is essential.This paper presents a case study of a novel method of extracting vehicle telemetry data from archived dashcam videos without the need to deploy conventional telemetry techniques. Utilising dashcam videos as input, and employing image processing and recognition technology, textual en-route driving data embedded in the video can be extracted. This data can then, in-turn, be used to model the performance of the vehicle, or an electric equivalent in terms of energy use and emissions. Results from preliminary testing with real-life dashcam videos, demonstrate negligible errors with regards to energy requirements and pollutants emitted from an EV operating on the modelled routes. Consequently, the proposed solution opens up the possibility to gather a significant amount of new data in order to better assess the transport sector’s energy requirements. This is especially important for situations where conventional telemetry is difficult to obtain. In addition, results from vehicle fleet modelling may inform policy decisions with regard to the impact of introducing low emission zones.

Study on the damage mechanism of hydraulic concrete under the alternating action of freeze-thaw and abrasion

This study investigates the damage mechanism of hydraulic concrete under the alternating action of freeze–thaw (F) and abrasion (W) in cold regions. To this end, three groups of working conditions were established with a constant water-binder ratio. The mass loss and relative dynamic elastic modulus (RDEM) of each cycle were evaluated using the fast-freezing method and underwater method. The phase composition and relative content change of hydration products, pore characteristic parameters and pore size distribution, morphology and microstructure of interfacial transition zone (ITZ) were analyzed by X-ray diffraction (XRD), mercury intrusion porosimeter(MIP), scanning electron microscopyn (SEM) and image recognition technology, respectively. The results indicate that the alternating effect will accelerate the mass loss of concrete and the reduction of the RDEM compared with the single factor. Following four consecutive alternating cycles, the mass loss rates of working conditions W and F group were found to be 4.61 % and 97.66 % lower, respectively, than those of working condition F-W group. The RDEM damage coefficient (γ < 1) was also observed. At the micro level, the freeze-thaw effect will result in the formation of a considerable number of micropores and the expansion of microcracks, with the abrasion effect continuing to promote this process. In the process of alternating action, the dissolution of Ca(OH)2 by external water through microcracks and interconnected pores is accelerated. Concurrently, the pores and microcracks in the ITZ expand, the morphology is roughened, and the microstructure is complicated. The interfacial bonding ability and compactness of different material components decrease. Under the action of external disturbance, cement paste and aggregate fall off, resulting in mass loss and a decreased RDEM.

Occupant-Detection-Based Individual Control of Four-Way Air Conditioner for Sustainable Building Energy Management

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings.

Quantitative assessment of human motion for health and rehabilitation: A novel fuzzy comprehensive evaluation approach

In the pursuit of advancing health and rehabilitation, the quintessence of human motion recognition technology has been underscored through its quantitative contributions to physical performance assessment. This research delineates the inception of a novel fuzzy comprehensive evaluation-based recognition method that stands at the forefront of such innovative endeavours. By synergistically fusing multi-sensor data and advanced classification algorithms, the proposed system offers a granular quantitative analysis with implications for health and fitness monitoring, particularly rehabilitation processes. Our methodological approach, grounded in the modal separation technique and Empirical Mode Decomposition (EMD), effectively distills the motion acceleration component from raw accelerometer data, facilitating the extraction of intricate motion patterns. Quantitative analysis revealed that our integrated framework significantly amplifies the accuracy of motion recognition, achieving an overall recognition rate of 90.03 %, markedly surpassing conventional methods, such as Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN), which hovered around 80 %. Moreover, the system demonstrated an unprecedented accuracy of 97 % in discerning minor left-right swaying motions, showcasing its robustness in evaluating subtle movement nuances—a paramount feature for rehabilitation and patient monitoring. This marked precision in motion recognition heralds a new paradigm in health assessment, enabling objective and scalable analysis pertinent to individualized therapeutic interventions. The experimental evaluation accentuates the system's adeptness at navigating the dichotomy between complex, intense motions and finer, subtler movements with a high fidelity rate. It substantiates the method's utility in delivering sophisticated, data-driven insights for rehabilitation trajectory monitoring.

A facial expression recognition network based on attention double branch enhanced fusion.

The facial expression reflects a person's emotion, cognition, and even physiological or mental state to a large extent. It has important application value in medical treatment, business, criminal investigation, education, and human-computer interaction. Automatic facial expression recognition technology has become an important research topic in computer vision. To solve the problems of insufficient feature extraction, loss of local key information, and low accuracy in facial expression recognition, this article proposes a facial expression recognition network based on attention double branch enhanced fusion. Two parallel branches are used to capture global enhancement features and local attention semantics respectively, and the fusion and complementarity of global and local information is realized through decision-level fusion. The experimental results show that the features extracted by the network are made more complete by fusing and enhancing the global and local features. The proposed method achieves 89.41% and 88.84% expression recognition accuracy on the natural scene face expression datasets RAF-DB and FERPlus, respectively, which is an excellent performance compared with many current methods and demonstrates the effectiveness and superiority of the proposed network model.