In order to realize the dynamic management of goods in the warehouse and improve the overall management efficiency and safety performance of the warehouse, a deeplearning and target detection technology for remote re...
详细信息
Gait recognition is a well-known biometric identification technology and is widely employed in different fields. Due to the advantages of deeplearning, such as self-learning capability, high accuracy and excellent ge...
详细信息
Gait recognition is a well-known biometric identification technology and is widely employed in different fields. Due to the advantages of deeplearning, such as self-learning capability, high accuracy and excellent generalization ability, various deep network algorithms have been applied in biometric recognition. Numerous studies have been conducted in this area;however, they may not always yield the expected outcomes owing to the issue of data imbalance in clinical and healthcare industries. To overcome this problem, deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image (HGR-DMCSCN) is proposed in this manuscript. Initially, the input images are taken from CASIA B and OU-ISIR datasets. Then the input images are given to preprocessing segment to enhance the superiority of the images based upon contrast-limited adaptive histogram equalization filtering (CLAHEF). Then preprocessed image is given to classification process using deep multi-convolutional stacked capsule network (DMCSCN) that is utilized for human gait detection under various conditions, like normal walking, carrying a bag and wearing a cloth. The proposed HGR-DMCSCN approach is executed in python and its performance is examined under performance metrics, such as F-Score, accuracy, RoC and computational time. Finally, the proposed approach attains 28.70%, 11.87% and 14.79% higher accuracy for CASIA B compared with existing methods.
In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detect...
详细信息
In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system's monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.
Due to the variety of lighting, postures, and occlusions, symmetry of faces and identification in an unrestricted area are difficult. The latest study demonstrates that deeplearning techniques can do remarkably well ...
详细信息
Due to the variety of lighting, postures, and occlusions, symmetry of faces and identification in an unrestricted area are difficult. The latest study demonstrates that deeplearning techniques can do remarkably well on these two challenges. The complex transmitted multitask structure the developers provide in this research takes advantage of the natural relationship between them to improve efficiency. The suggested Multi-task Cascaded Mask Convolutional Network (MTCMCN) has three layers of carefully planned deep convolution networks that work together to figure out where faces and landmarks are from a wide range of angles. Additionally, they provide a novel, continuous, difficult sample mining approach for learning procedures, which may automatically boost efficiency without the manual choice of samples. The use of a sizable cross-age image collection containing gender and age descriptors advances the creation of Age-Invariant Face Recognition (AIFR) and FAS. MTCMCN outperforms existing methods by achieving state-of-the-art accuracy on benchmarks like FDDB and WIDER FACE, exceeding 95% accuracy in some cases. It has a Central processing Unit (CPU) speed of 16 frames per second and a GPU speed of 99 frames per second, ensuring real-time performance. The proposed system achieves this by using a special identification conditional block and live hard sample mining, thereby improving face recognition regardless of age.
As modern power systems continue to evolve, accurate power load forecasting remains a critical issue in energy management. The phase space reconstruction (PSR) method can effectively retain the inner chaotic property ...
详细信息
As modern power systems continue to evolve, accurate power load forecasting remains a critical issue in energy management. The phase space reconstruction (PSR) method can effectively retain the inner chaotic property of power load from a system dynamics perspective and thus is a promising knowledge-based method for power load forecasting. To fully leverage the PSR method's capability in modeling this high-dimensional, non-stationary characteristics of power load data, and to address the challenges faced by its classical mathematical prediction algorithms ineffectively solving contemporary prediction scenarios characterized by massive volumes of data. This study proposes a novel learning-based multi-step forecasting approach that utilizes an image-based modeling perspective for the reconstructed phase trajectories. Firstly, the feature engineering approach that simultaneously utilizes dynamic evolution features and temporal locality features in the trajectory image is proposed. Through mathematical derivation, the equivalent characterization of the PSR method and another time series modeling approach, patch segmentation (PS), is demonstrated for the first time. Building on this prior knowledge, a novel image-based modeling perspective incorporating a global and local feature extraction strategy is introduced to fully leverage these valuable features. Subsequently, within this framework, a novel deeplearning model, termed PSR-GALIEN, is designed for end-to-end processing. This model employs a Transformer Encoder and 2D convolutional neural networks (CNNs) to extract global and local patterns from the image, while a multi-layer perceptron (MLP)-based predictor is utilized for efficient correlation modeling. Extensive experiments on five real-world datasets show that PSR-GALIEN consistently outperforms six state-of-the-art deeplearning models in short-term load forecasting scenarios with varying characteristics, demonstrating its great robustness. Ablation studies fur
In response to the challenge of effectively identifying artificially generated images from real ones, this paper proposes a deeplearning-based approach for authenticating images. The proposed method utilizes a combin...
详细信息
real-time pothole detection serves a crucial role in maintaining road infrastructure and ensuring the safety of drivers. Traditional methods for identifying potholes are often labour-intensive and inefficient, prompti...
详细信息
A novel methodology for first break picking (FBP) based on deeplearning algorithms is proposed in this paper. The goal of this study is to automate FBP by application of neural networks trained on synthetic seismic d...
详细信息
Rapid drying of soil leads to its fracture. The cracks left behind by these fractures are best seen in soils such as clays that are fine in the texture and shrink on drying, but this can be seen in other soils too. He...
详细信息
Rapid drying of soil leads to its fracture. The cracks left behind by these fractures are best seen in soils such as clays that are fine in the texture and shrink on drying, but this can be seen in other soils too. Hence, different soils from the same region show different characteristic desiccation cracks and can thus be used to identify the soil type. In this paper, three types soils namely clay, silt, and sandy-clay-loam from the Brahmaputra river basin in India are studied for their crack patterns using both conventional studies of hierarchical crack patterns using Euler numbers and fractal dimensions, as well as by applying deep-learning techniques to the images. Fractal dimension analysis is found to be an useful pre-processing tool for deeplearningimage analysis. Feed forward neural networks with and without data augmentation and with the use of filters and noise suggest that data augmentation increases the robustness and improves the accuracy of the model. Even on the introduction of noise, to mimic a real-life situation, 92.09% accuracy in identification of soil was achieved, proving the combination of conventional studies of desiccation crack images with deeplearning algorithms to be an effective tool for identification of real soil types.
Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation signi...
详细信息
Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deeplearning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.
暂无评论