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Research on sports image classification method based on SE-RES-CNN model.

Qinglan Li1, Jichong Lei2, Changan Ren3

  • 1Department of Physical Education and Research, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.

Scientific Reports
|August 17, 2024
PubMed
Summary

A new SE-RES-CNN model enhances sports image classification accuracy by 5% using adaptive channel weighting and deep feature extraction. This deep learning approach significantly improves the efficiency of sports image retrieval and management.

Keywords:
Computer visionDeep learningSE-RES-CNN modelSports image classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Manual sports image classification is inefficient and inaccurate for large datasets.
  • Advancements in digital technologies necessitate efficient image classification methods.

Purpose of the Study:

  • To develop an efficient deep learning model for sports image classification.
  • To improve the accuracy and speed of sports image retrieval and management.

Main Methods:

  • Introduction of a novel SE-RES-CNN neural network model.
  • Integration of an SE module for adaptive channel weight adjustment.
  • Utilizing a Res module for deep feature extraction, gradient vanishing prevention, and multi-scale processing.

Main Results:

  • The SE-RES-CNN model achieved up to 98% accuracy on a sports image dataset.
  • Demonstrated a 5% improvement in classification accuracy compared to VGG-16 and ResNet50.
  • Classified 100 images in 6 seconds with a single prediction time of 0.012 seconds.

Conclusions:

  • The SE-RES-CNN model is accurate and effective for sports image classification.
  • The proposed model significantly enhances the efficiency of sports image retrieval.
  • Validated the model's capability in handling large-scale image datasets.