Object detection is one of the most important and challenging branches of computer vision, whose main task is to classify and localize objects in images or videos. The development of object detection ...technology has been more than 20 years, from the early traditional detection methods to the current deep learning methods, the improvement of object detection accuracy and speed stems from the rapid development of deep learning technology. Traditional object detection techniques have many limitations, and using convolutional neural networks as the main framework for object detection can efficiently extract features and reduce the complexity of manual feature extraction. To comprehensively and deeply understand the development status of object detection, based on the research of domestic and foreign related literature, this paper reviews the research background of object detection, introduces the problems and dilemmas faced by traditional object detection algorithms, and analyzes the current mainstream object detection algorithms. This paper mainly carries out the relevant algorithms from three perspectives: Anchor-based, Anchor-free, and Transformer-based, and summarizes their structure, performance, advantages, and disadvantages in detail. This paper also introduces the commonly used datasets and related performance evaluation indexes for object detection, as well as the applications of object detection in industrial, transportation, medical, and other fields. According to the current research hotspots and the development trend of related technologies, the future research direction of object detection is prospected.
In this paper, the omnidirectional receiver (Rx) of wireless power transfer (WPT) system is proposed, making any transmitter (Tx) into omnidirectional Tx. The omnidirectional Rx is composed of the ...quadrature double‐D (DD) coils and circular coil in series (C2DD). The C2DD Rx can utilize the toroidal magnetic field (TMF) and the linear magnetic field (LMF). The paper presents the circuit diagram model of WPT system. Based on the analysis of circuit model, the smooth mutual inductance of system is responsible for stable transmission performance during C2DD Rx movement. The result of numerical calculation and magnetic field simulation verifies the omnidirectional capability of the C2DD Rx with circular Tx as an example. Experiment shows that the C2DD Rx receives power in any direction. The C2DD Rx can receive maximum transmission power of 7.1 W with maximum transmission efficiency of 59%.
The omnidirectional Rx is composed of the quadrature double‐D (DD) coils and circular coil in series (C2DD). The C2DD Rx can utilize the toroidal magnetic field (TMF) and the linear magnetic field (LMF). The C2DD Rx can receive maximum transmission power of 7.1 W with maximum transmission efficiency of 59%.
With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and ...more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple-scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers’ head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet-based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers’ angle, scale, and occlusion.
The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the ...electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.
This study aims to determine whether CYP2C19 loss‐of‐function (LoF) variants were associated with long‐term ischemic stroke risk in Chinese primary care patients treated with clopidogrel. Patients ...treated with clopidogrel were ascertained from Chinese electronic medical records linked with a biobank for a retrospective cohort study. Their medical information was examined for the period from January 2018 to December 2021. Two CYP2C19 major loss of function variants (*2:rs4244285 and *3: rs4986893) were genotyped. The clinical outcome was ischemic stroke event. Cox regression analysis was used to evaluate the association between the occurrence of ischemic stroke events and CYP2C19 LoF variants. Covariates included age, gender, body mass index, prior ischemic stroke, transient ischemic attack, hypertension, diabetes mellitus, hyperlipoidemia, smoke status, aspirin use, proton‐pump inhibitor use, and statin use. Of the 1,141 patients included in the clopidogrel therapy cohort, 61.9% carried at least one CYP2C19 LoF variant. During a median follow‐up period of 12 months, 103 patients (9.0%) had an ischemic stroke. After adjusting for other risk factors, carriers of CYP2C19 LoF variants had significantly higher risk of ischemic stroke compared with non‐carriers (hazard ratio: 1.64, 95% confidence interval: 1.06–2.53, P = 0.025). This pharmacogenetic study of clopidogrel provides novel insights into the association between the CYP2C19 LoF variant and long‐term stroke risk. We established that there is still a need for CYP2C19 genotype‐guided personalized antiplatelet therapy in those who have returned to the primary care setting for clopidogrel prescription.
The increased frequency of forest fires in recent years has raised concerns about the high cost associated with traditional forest fire prevention methods. To address this issue, this paper presents ...a novel forest fire detection and segmentation application for UAV-assisted mobile edge computing system. Traditional target detection and segmentation systems for forest fire detection are often large and unsuitable for deployment on edge equipment such as UAVs. Deploying such models on edge gateways can also lead to high costs and delays. To overcome these challenges, this paper proposes a lightweight fire target detection and precision segmentation model that can be used on UAVs and other edge equipment. The proposed algorithm achieves more accurate image segmentation, thereby improving the efficiency of fire location. Additionally, an edge computing system is built to link the feedback of the edge model with the edge gateway, administrators, and other intelligent devices promptly. Extensive experiments with large datasets and in real environments demonstrate the efficacy of the proposed algorithm, effectively enhancing the efficiency of forest inspection and forest fire warning capabilities.
PDF
