scholarly journals Research on Real-Time Detection of Sprint Error Based on Visual Features and Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengming Chen ◽  
Junyong Li
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Error Detection ◽  
Three Dimensional ◽  
Detection Methods ◽  
Visual Features ◽  
Detection Accuracy ◽  
Detection Time ◽  
Visual Characteristics ◽  
Internet Of Things Technology

The current sprint error detection methods do not consider the analysis of the visual characteristics of sprint error, which leads to low detection accuracy, long detection time, and poor detection stability. To overcome this defect, inspired by Internet of Things technology, a real-time sprint error detection method based on visual characteristics is proposed. Based on the basic principle of RFID action perception, the original phase data is preprocessed, the channel parameters are selected, and the tag layout is optimized to form the action-oriented feature. Based on the three-dimensional visual features, the three-dimensional coordinate points of the sports field are determined, and the movement features of the sprint are extracted and formally described. Based on the analysis of the visual characteristics of sprint errors, the block pheromones of single frame sprint motion edge contour are obtained for clustering, and the sprint errors’ feature information is obtained and filtered. Sift technology is used to obtain the boundary contour line and implement the corner characteristics. The Hessian matrix of contour wave domain edge detection is used to calculate the contour wave domain matrix of the image and draw the contour curve of the image of sprint error action to realize the detection of sprint error action. The experimental results show that the proposed method has good stability and can effectively improve the detection accuracy and shorten the detection time.

scholarly journals Sistem Peringatan Dini Banjir Secara Real-Time Berbasis Web Menggunakan Arduino dan Ethernet

2017 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Dedi Satria ◽  
Syaifuddin Yana ◽  
Rizal Munadi ◽  
Saumi Syahreza
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Real Time ◽  
Early Warning ◽  
Major Influence ◽  
Google Maps ◽  
Flood Monitoring ◽  
Arduino Uno ◽  
Internet Of Things Technology ◽  
Monitoring Information

a b s t r a c tThe development of flood early warning technology has grown rapidly. The technology has led to an increase in technology in terms of communication and information. Internet of Things technology (IoTs) has provided a major influence on the development of early warning information system. In this article a protipe-based flood monitoring information system of Google Maps have been designed by integrating Ultrasonic sensors as the height of the detector, the Arduino Uno as a processor, U-Blox GPS modules Neo 6 m GSM module and as the sender of data is the height of the water and the coordinates to the station of the system informais flood. The design of the prototype produces information flood elevations along with location based Google Maps interface.Keywords:Flood, Arduino, Internet of Things Technology (IoTs), Ethernet a b s t r a kPengembangan teknologi peringatan dini banjir telah tumbuh dengan cepat. Teknologi tersebut telah mengarah kepada peningkatan di segi teknologi komunikasi dan informasi. Teknologi Internet of Things (IoTs) telah memberikan pengaruh besar terhadap perkembangan sistem informasi peringatan dini. Didalam artikel ini sebuah protipe sistem informasi monitoring banjir berbasis Google Maps telah dirancang dengan mengintegrasikan sensor ultrasonik sebagai pendeteksi ketinggian, Arduino Uno sebagai pemroses, modul GPS U-Blox Neo 6m dan modul GSM sebagai pengirim data ketinggian air dan koordinat ke stasion sistem informais banjir. Perancangan prototipe menghasilkan informasi ketinggian banjir beserta lokasinya berbasis antarmuka Google Maps.Kata Kunci: Banjir, Arduino, Internet of Things Technology (IoTs), Ethernet

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

Analysis of Things Technology to Promote the Dynamic Alliance Virtual Performance Evaluation

2014 ◽  
Vol 989-994 ◽  
pp. 5041-5044
Author(s):  
Hong Xia Zhang ◽  
Qiang Zhang
Keyword(s):  
Performance Evaluation ◽  
Internet Of Things ◽  
Real Time ◽  
Communication Technology ◽  
Data Communication ◽  
The Internet ◽  
Dynamic Alliance ◽  
Time Performance ◽  
Development Goals ◽  
Internet Of Things Technology

With the development of modern information and communication technology and network technology, modern enterprises choose to build virtual dynamic alliance to achieve business development goals. There are many uncertain relationships between the participating companies in the virtual dynamic alliance. These companies join together to accomplish a particular goal, but also has their own unique individual needs. Internet of Things is an important part of a new generation of information technology. Things technology is an extension and expansion of the network based on the Internet. Basis for building virtual dynamic alliance is the Internet. The enterprises participating in a virtual dynamic alliance communicate with each other relying on information communication technology. Internet of Things technology provides real-time performance evaluation data to the participating enterprises. This will ensure real-time performance data communication between the dynamic alliance enterprises relatively loose.

Facilities Vegetables Monitoring and Control System Design Based on WCF and Internet of Things

2014 ◽  
Vol 945-949 ◽  
pp. 2637-2640 ◽  
Author(s):  
Li Yan ◽  
Guo Wei Wang ◽  
Shui Fang Wu
Keyword(s):  
Environmental Factors ◽  
Internet Of Things ◽  
Real Time ◽  
High Yield ◽  
Monitoring And Control ◽  
Real Time Monitoring ◽  
Communication Interface ◽  
Temperature And Humidity ◽  
Sql Server Database ◽  
Internet Of Things Technology

The production of facilities vegetable affected significantly by environmental factors, therefore it require real-time monitoring information of light, air temperature and humidity, soil temperature and humidity, and timely early warning and controlling, to prevent irreparable damage. This study achieved real-time monitoring and timely warning of environmental factors, and optimal control of pumps and roller blinds in the Facility vegetable. We have adopted Internet of Things technology and WCF communication interface, the system through C # language and the SQL Server database to prepare. The application in Jilin Province Jinta Industrial (Group) Co.Ltd. has showed that the system is better, achieved the purpose of high yield income.

scholarly journals Fast pupil tracking based on the study of a boundary-stepped image model and multidimensional optimization Hook-Jives method

2021 ◽  
Vol 20 (2) ◽  
pp. 435-462
Author(s):  
Yuriy Grushko ◽  
Roman Parovik
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Detection Accuracy ◽  
Fast Method ◽  
Image Model ◽  
Dimensional Parameter ◽  
Step Model ◽  
Brightness Difference ◽  
And Mathematics ◽  
Pupil Tracking

A new fast method for pupil detection and eyetracking real time is being developed based on the study of a boundary-step model of a grayscale image by the Laplacian-Gaussian operator and finding a new proposed descriptor of accumulated differences (point identifier), which displays a measure of the equidistance of each point from the boundaries of some relative monotonous area (for example, the pupil of the eye). The operation of this descriptor is based on the assumption that the pupil in the frame is the most rounded monotonic region with a high brightness difference at the border, the pixels of the region should have an intensity less than a predetermined threshold (but the pupil may not be the darkest region in the image). Taking into account all of the above characteristics of the pupil, the descriptor allows achieving high detection accuracy of its center and size, in contrast to methods based on threshold image segmentation, based on the assumption of the pupil as the darkest area, morphological methods (recursive morphological erosion), correlation or methods that investigate only the boundary image model (Hough transform and its variations with two-dimensional and three-dimensional parameter spaces, the Starburst algorithm, Swirski, RANSAC, ElSe). The possibility of representing the pupil tracking problem as a multidimensional unconstrained optimization problem and its solution by the Hook-Jeeves non-gradient method, where the function expressing the descriptor is used as the objective function, is investigated. In this case, there is no need to calculate the descriptor for each point of the image (compiling a special accumulator function), which significantly speeds up the work of the method. The proposed descriptor and method were analyzed, and a software package was developed in Python 3 (visualization) and C ++ (tracking kernel) in the laboratory of the Physics and Mathematics Faculty of Kamchatka State University of Vitus Bering, which allows illustrating the work of the method and tracking the pupil in real time.

scholarly journals Real-time Factory Smoke Detection Based on Two-stage Relation-guided Algorithm

2021 ◽  
Author(s):  
Zhenyu Wang ◽  
Senrong Ji ◽  
Duokun Yin
Keyword(s):  
Real Time ◽  
Contextual Information ◽  
Search Space ◽  
Detection Algorithm ◽  
Image Resolution ◽  
Single Stage ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
Two Stage

Abstract Recently, using image sensing devices to analyze air quality has attracted much attention of researchers. To keep real-time factory smoke under universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. Since most smoke images in real scenes have challenging variances, it’s difficult for existing object detection methods. To this end, we introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the shortcomings of the single-stage method. Experimental results show that the TSSD algorithm can robustly improve the detection accuracy of the single-stage method and has good compatibility for different image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy AP mean of the TSSD model reaches 59.24%, even surpassing the current detection model Faster R-CNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), which meets the real-time requirements, and can be deployed in the mobile terminal carrier. This model can be widely used in some scenes with smoke detection requirements, providing great potential for practical environmental applications.

scholarly journals Ship Detection Based on YOLOv2 for SAR Imagery

2019 ◽  
Vol 11 (7) ◽  
pp. 786 ◽  
Author(s):  
Yang-Lang Chang ◽  
Amare Anagaw ◽  
Lena Chang ◽  
Yi Wang ◽  
Chih-Yu Hsiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Experimental Results ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Single Shot ◽  
Ship Detection ◽  
Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

scholarly journals Design of smart agriculture based on big data and Internet of things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091706 ◽  
Author(s):  
Chunling Li ◽  
Ben Niu
Keyword(s):  
Data Mining ◽  
Information Processing ◽  
Internet Of Things ◽  
Real Time ◽  
Data Communication ◽  
Large Data ◽  
Time Data ◽  
Real Time Data ◽  
Smart Agriculture ◽  
Internet Of Things Technology

With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

scholarly journals Large-Aperture Real-Time Compensated Collimating Wavefront Error Detection Method

2019 ◽  
Vol 9 (11) ◽  
pp. 2370
Author(s):  
Xueliang Zhu ◽  
Dasen Wang ◽  
Fengming Nie ◽  
Bingcai Liu ◽  
Hongjun Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Detection Method ◽  
Three Dimensional ◽  
Surface Measurement ◽  
Dimensional Error ◽  
Detection Technology ◽  
Wavefront Error ◽  
Scanning Detection ◽  
Wavefront Detection

This paper proposes a real-time compensated pentaprism scanning wavefront detection method to achieve real-time compensation for scanning errors occurring during prism movement along a guide rail. The method is based on existing pentaprism scanning wavefront detection technology and it is realized by applying self-collimation-based three-dimensional error compensation. Using theoretical and data analyses of a detection experiment, the reliability of the optimized pentaprism scanning detection method is verified, thus effectively ensuring the reasonable estimation of the interferometry surface measurement uncertainty.

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