RETRACTED: Beach sports image detection based on heterogeneous multi-processor and convolutional neural network

Liu, Wei

doi:10.1016/j.micpro.2021.103910

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Thirdly, the 2000 × 4096-dimensional image feature vector is input into the SVM classifier for category judgement. Besides, the appearance of the detection box is processed by NMS (Non-Maximum Suppression) [12,13]. Finally, the untested detection box is corrected using the regression according to the different categories, to obtain the final detection results for output.…”

Section: Research Methods Based On Region Proposalmentioning

confidence: 99%

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Liu

et al. 2021

PLoS ONE

265

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This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

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Section: Research Methods Based On Region Proposalmentioning

confidence: 99%

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Liu

et al. 2021

PLoS ONE

265

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“…In sports such as table tennis [200], squash [201,202], and golf [203], ball detection and tracking and player pose detection [204] are challenging tasks. In ball-centric sports such as rugby, American football, handball, baseball, ball/player detection [205][206][207][208][209][210][211] and tracking [212][213][214][215][216][217][218][219][220][221], analyzing the action of the player [23,[222][223][224][225][226][227], event detection and classification [228][229][230][231][232], performance analysis of player [233][234][235], referee identification and gesture recognition are the major challenging tasks. Video highlight generation is a subclass of video summarization [236][237][238][239] which may be viewed as a subclass of sports video analysis.…”

Section: Miscellaneousmentioning

confidence: 99%

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

2022

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Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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Application of recurrent neural network in predicting athletes' sports achievement

Hou

Tian

2021

J Supercomput

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RETRACTED: Beach sports image detection based on heterogeneous multi-processor and convolutional neural network

Cited by 5 publications

References 5 publications

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Application of recurrent neural network in predicting athletes' sports achievement

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