Comparison of the Effect of Interpolation on the Mask R-CNN Model

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Abstract

Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer

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Ahn, Y. P., Kim, K. B., & Park, H. J. (2023). Comparison of the Effect of Interpolation on the Mask R-CNN Model. Journal of Information and Communication Convergence Engineering, 21(1), 17–23. https://doi.org/10.56977/jicce.2023.21.1.17

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