Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

Yang, Kai

doi:10.32604/csse.2023.037266

Cited by 2 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a comparison is made between the results obtained from the proposed DNN/NTSO model and those achieved by other state-of-the-art models including as Attention-based graph convolution-guided third-order hourglass network (AGTH-Net) 6 , particle swarm optimization algorithm (PSO) 7 , YOLOv5 backbone and SPD-Conv 8 , and Depth Learning (DL) 9 . In the following,the mathematical formulation for measurements have been given:…”

Section: Model Analysismentioning

confidence: 99%

See 1 more Smart Citation

Improved sports image classification using deep neural network and novel tuna swarm optimization

Zhou,

Zhang,

Effatparvar

2024

Sci Rep

View full text Add to dashboard Cite

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the application of NTSO to the DNN, a finely-tuned network is developed, exhibiting exceptional performance in sports image classification. Rigorous experiments have been conducted on an extensive dataset of sports images, and the obtained results have been compared against other state-of-the-art methods, including Attention-based graph convolution-guided third-order hourglass network (AGTH-Net), particle swarm optimization algorithm (PSO), YOLOv5 backbone and SPD-Conv, and Depth Learning (DL). According to a fivefold cross-validation technique, the DNN/NTSO model provided remarkable precision, recall, and F1-score results: 97.665 ± 0.352%, 95.400 ± 0.374%, and 0.8787 ± 0.0031, respectively. Detailed comparisons reveal the DNN/NTSO model's superiority toward various performance metrics, solidifying its standing as a top choice for sports image classification tasks. Based on the practical dataset, the DNN/NTSO model has been successfully evaluated in real-world scenarios, showcasing its resilience and flexibility in various sports categories. Its capacity to uphold precision in dynamic settings, where elements like lighting, backdrop, and motion blur are prominent, highlights its utility. The model's scalability and efficiency in analyzing images from live sports competitions additionally validate its suitability for integration into real-time sports analytics and media platforms. This research not only confirms the theoretical superiority of the DNN/NTSO model but also its pragmatic effectiveness in a wide array of demanding sports image classification assignments.

show abstract

Section: Model Analysismentioning

confidence: 99%

“…Yang et al 9 proposed a new approach to identifying and positioning table tennis using a convolutional neural network (CNN). The method effectively overcame the limitations of existing techniques that rely on color and contour features, which lack adaptability in diverse environments.…”

mentioning

confidence: 99%

Improved sports image classification using deep neural network and novel tuna swarm optimization

Zhou,

Zhang,

Effatparvar

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

A study on the application of neural style transformation technique in personalized art creation

Zhao

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In this paper, using the loss function of image stylization, combined with the development of image stylization based on deep learning, the proposed convolutional neural network in the style of image conversion fitting and overfitting response. Based on this, the multi-scale feature fusion method is chosen to train the style conversion network, with the help of deep feature extraction of the image for style conversion, reconstruct the multi-scale feature fusion image, and send it to the decoder for deep coding to realize the style conversion. To evaluate the effectiveness of the proposed multi-scale feature fusion style conversion algorithm, the content loss and style loss parameters of the algorithm are analyzed using the lightweight encoder and VGG encoder, respectively. Calculate the number of algorithmic model parameters and the amount of computation. Analyze the change in the iteration number of the personalized art style conversion process and select the performance evaluation index to evaluate the results of the personalized art style image conversion process. In the process of personalized art style conversion, the multi-scale feature fusion algorithm network proposed in this paper can basically reconstruct the original image after 1000 iterations. Personalized style image reconstruction has a PSNR of 25.26 dB when the number of iterations is 1000. With the deepening of training, the reconstruction effect becomes better and better, and the advantages of personalized art style image conversion applications are significant.

show abstract

Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

Cited by 2 publications

References 31 publications

Improved sports image classification using deep neural network and novel tuna swarm optimization

Improved sports image classification using deep neural network and novel tuna swarm optimization

A study on the application of neural style transformation technique in personalized art creation

Contact Info

Product

Resources

About