The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Liang, Chen; Liang, Zhijun

doi:10.1109/access.2022.3221530

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In the present study, the classification of electronic components is made. The classification of electronic components is important for many areas, such as usage areas, industrial studies, and the recycling and production of large electronic devices [3,4].…”

Section: Introductionmentioning

confidence: 99%

Classification of Electronic Components Based on Convolutional Neural Network Architecture

Atik

2022

Energies

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Electronic components are basic elements that are widely used in many industrial and technological fields. With the development of technology, their dimensions are being produced in smaller and smaller sizes. As a result, making fast distinctions becomes difficult. Being able to classify electronic components quickly and accurately will save labor and time in all areas where these elements are used. Recently, deep learning algorithms have become preferential in product classification studies due to their high accuracy and speed. In this paper, a classification study of electronic components was carried out with the deep learning method. A new convolutional neural network (CNN) model is proposed in the study. The model has six convolution layers, four pooling layers, two fully connected layers, softmax, and a classification layer. The training parameters of the network were determined as an ensemble size of 16, maximum period of 100, initial learning rate of 1 × 10−3, and the optimizing method sgdm. While determining the CNN model layers and training parameters, the values with the highest predictive values were selected as a result of the trials. Classification research was conducted using the pre-trained networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet for the same data, and their performance success parameters were compared to those of the proposed CNN model. The proposed CNN model showed higher performance than the other methods, and an accuracy value of 98.99% was obtained.

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Section: Introductionmentioning

confidence: 99%

Classification of Electronic Components Based on Convolutional Neural Network Architecture

Atik

2022

Energies

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Cross-modal self-attention mechanism for controlling robot volleyball motion

Wang,

Liang

2023

Front. Neurorobot.

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IntroductionThe emergence of cross-modal perception and deep learning technologies has had a profound impact on modern robotics. This study focuses on the application of these technologies in the field of robot control, specifically in the context of volleyball tasks. The primary objective is to achieve precise control of robots in volleyball tasks by effectively integrating information from different sensors using a cross-modal self-attention mechanism.MethodsOur approach involves the utilization of a cross-modal self-attention mechanism to integrate information from various sensors, providing robots with a more comprehensive scene perception in volleyball scenarios. To enhance the diversity and practicality of robot training, we employ Generative Adversarial Networks (GANs) to synthesize realistic volleyball scenarios. Furthermore, we leverage transfer learning to incorporate knowledge from other sports datasets, enriching the process of skill acquisition for robots.ResultsTo validate the feasibility of our approach, we conducted experiments where we simulated robot volleyball scenarios using multiple volleyball-related datasets. We measured various quantitative metrics, including accuracy, recall, precision, and F1 score. The experimental results indicate a significant enhancement in the performance of our approach in robot volleyball tasks.DiscussionThe outcomes of this study offer valuable insights into the application of multi-modal perception and deep learning in the field of sports robotics. By effectively integrating information from different sensors and incorporating synthetic data through GANs and transfer learning, our approach demonstrates improved robot performance in volleyball tasks. These findings not only advance the field of robotics but also open up new possibilities for human-robot collaboration in sports and athletic performance improvement. This research paves the way for further exploration of advanced technologies in sports robotics, benefiting both the scientific community and athletes seeking performance enhancement through robotic assistance.

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The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Cited by 2 publications

References 26 publications

Classification of Electronic Components Based on Convolutional Neural Network Architecture

Classification of Electronic Components Based on Convolutional Neural Network Architecture

Cross-modal self-attention mechanism for controlling robot volleyball motion

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