Optimal Deep Neural Network Modified by Advanced Seasons Optimizer for Classification of Olympic Sports Images

Author:

Wang Yanbu1,Wang Chao2,Wang Yongkang3,Liu Linqing4

Affiliation:

1. School of Strength and Conditioning, Beijing Sport University, Beijing 100084, China

2. Institute of Competitive Sports, Beijing Sport University, Beijing 100084, China

3. School of Physical Education, North University of China, Taiyuan 030051, China

4. Department of Physical Education, Peking University, Beijing 100871, China

Abstract

Sports image classification has gained increasing significance in various domains, including athlete training, event management, and broadcasting. Nonetheless, accurately classifying these images remains a formidable challenge due to factors such as variations in lighting conditions, pose, and attire. This paper proposes an advanced deep learning approach to address this issue. The proposed method uses a modified Inception-V4 (IV4) architecture, which has exhibited remarkable success in image classification tasks. To further enhance the performance of the IV4 model, an advanced version of the seasons optimizer (ASO), a potent optimization algorithm, has been introduced, which adjusts the learning rate during training. This modified optimizer enables the IV4 model to converge more rapidly and achieve higher accuracy. An evaluation of the proposed method has been conducted using a comprehensive dataset comprising Olympic sports images. The experimental results demonstrate significant enhancements over existing approaches, attaining superior performance across multiple evaluation metrics. These findings underscore the potential of combining cutting-edge deep neural networks with advanced optimization techniques for the precise and efficient classification of sports images.

Publisher

MDPI AG

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