Optimizing semantic segmentation tasks for soccer robots: an empirical study on deep learning model selection and hyperparameter tunings

Lexuan Li

doi:10.1117/12.3036336

16 August 2024 Optimizing semantic segmentation tasks for soccer robots: an empirical study on deep learning model selection and hyperparameter tunings

Lexuan Li

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132300S (2024) https://doi.org/10.1117/12.3036336
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

This study aims to enhance the performance and applicability of soccer robots in semantic segmentation tasks. Initially, we prepared the data and selected three models, DenseNet121, ResNet50, and Mobilenetv2, for training. During the training process, we employed data augmentation methods such as image rotation, horizontal flipping, and cropping, and experimented with various loss functions and optimizers. The experimental results showed that the combination of Mean Squared Error (MSE) as the loss function and Adam as the optimizer performed best. Additionally, we explored the addition of attention mechanisms and freezing layers, and introduced a dynamic learning rate strategy. In terms of model selection, MobileNetv2 was considered the optimal model due to its high validation accuracy, reasonable training time, and resource usage. In the final stage, we trained the final model based on the excellent parameters and models summarized earlier, achieving a validation accuracy of 94.31% in the last epoch. Overall, our research provides an effective strategy to optimize the semantic segmentation tasks of soccer robots.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lexuan Li "Optimizing semantic segmentation tasks for soccer robots: an empirical study on deep learning model selection and hyperparameter tunings", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132300S (16 August 2024); https://doi.org/10.1117/12.3036336

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KEYWORDS

Education and training

Image segmentation

Performance modeling

Data modeling

Robots

Semantics

Mathematical optimization

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