Unsupervised Domain Adaptation Approaches for Chessboard Recognition
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929551554117632 |
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| author | Jabbour, Wassim Benoit-Jeannin, Enzo Bedford, Oscar Shahin, Saif |
| author_facet | Jabbour, Wassim Benoit-Jeannin, Enzo Bedford, Oscar Shahin, Saif |
| contents | Chess involves extensive study and requires players to keep manual records of their matches, a process which is time-consuming and distracting. The lack of high-quality labeled photographs of chess boards, and the tediousness of manual labeling, have hindered the wide application of Deep Learning (DL) to automating this record-keeping process. This paper proposes an end-to-end pipeline that employs domain adaptation (DA) to predict the labels of real, top-view, unlabeled chessboard images using synthetic, labeled images. The pipeline is composed of a pre-processing phase which detects the board, crops the individual squares, and feeds them one at a time to a DL model. The model then predicts the labels of the squares and passes the ordered predictions to a post-processing pipeline which generates the Forsyth-Edwards Notation (FEN) of the position. The three approaches considered are the following: A VGG16 model pre-trained on ImageNet, defined here as the Base-Source model, fine-tuned to predict source domain squares and then used to predict target domain squares without any domain adaptation; an improved version of the Base-Source model which applied CORAL loss to some of the final fully connected layers of the VGG16 to implement DA; and a Domain Adversarial Neural Network (DANN) which used the adversarial training of a domain discriminator to perform the DA. Also, although we opted not to use the labels of the target domain for this study, we trained a baseline with the same architecture as the Base-Source model (Named Base-Target) directly on the target domain in order to get an upper bound on the performance achievable through domain adaptation. The results show that the DANN model only results in a 3% loss in accuracy when compared to the Base-Target model while saving all the effort required to label the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15206 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Domain Adaptation Approaches for Chessboard Recognition Jabbour, Wassim Benoit-Jeannin, Enzo Bedford, Oscar Shahin, Saif Computer Vision and Pattern Recognition Machine Learning Chess involves extensive study and requires players to keep manual records of their matches, a process which is time-consuming and distracting. The lack of high-quality labeled photographs of chess boards, and the tediousness of manual labeling, have hindered the wide application of Deep Learning (DL) to automating this record-keeping process. This paper proposes an end-to-end pipeline that employs domain adaptation (DA) to predict the labels of real, top-view, unlabeled chessboard images using synthetic, labeled images. The pipeline is composed of a pre-processing phase which detects the board, crops the individual squares, and feeds them one at a time to a DL model. The model then predicts the labels of the squares and passes the ordered predictions to a post-processing pipeline which generates the Forsyth-Edwards Notation (FEN) of the position. The three approaches considered are the following: A VGG16 model pre-trained on ImageNet, defined here as the Base-Source model, fine-tuned to predict source domain squares and then used to predict target domain squares without any domain adaptation; an improved version of the Base-Source model which applied CORAL loss to some of the final fully connected layers of the VGG16 to implement DA; and a Domain Adversarial Neural Network (DANN) which used the adversarial training of a domain discriminator to perform the DA. Also, although we opted not to use the labels of the target domain for this study, we trained a baseline with the same architecture as the Base-Source model (Named Base-Target) directly on the target domain in order to get an upper bound on the performance achievable through domain adaptation. The results show that the DANN model only results in a 3% loss in accuracy when compared to the Base-Target model while saving all the effort required to label the data. |
| title | Unsupervised Domain Adaptation Approaches for Chessboard Recognition |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.15206 |