지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 1,136 - 1,146 (11page)
- DOI
- 10.5909/JBE.2024.29.7.1136
이용수
초록· 키워드
오류제보하기
With the advancement of autonomous driving technology, the importance of semantic segmentation has markedly increased, while the amount of datasets needed for training has been limited. Accordingly, there has been a growing effort to increase datasets using data augmentation techniques to train semantic segmentation models. However, the distributional gap between augmented and real data can lead to performance limitations when models trained on real data are applied to augmented data. Therefore, this paper constructs new datasets by applying proposed data transformations on real-world datasets. Additionally, we evaluate the impact of these transformations on semantic segmentation models trained on real datasets. Results show that semantic segmentation models are vulnerable to distortions in color information and object characteristics in transformed datasets. Furthermore, the vision transformer based model is less sensitive to distribution changes and shows greater segmentation performance compared to fully convolutional network based models.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Related Work
Ⅲ. Proposed Method
Ⅳ. Experiment Result
Ⅴ. Conclusion
References
참고문헌 (23)
참고문헌 신청
Siam, Mennatullah, et al. “Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges.” 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), IEEE, Oct 2017. doi: https://doi.org/10.1109/ITSC.2017.8317714
Meletis, Panagiotis. “Towards holistic scene understanding: Semantic segmentation and beyond.” arXiv preprint arXiv:2201.07734, Jan 2022. doi: https://doi.org/10.48550/arXiv.2201.07734
Chen, Yuhua, Wen Li, and Luc Van Gool. “Road: Reality oriented adaptation for semantic segmentation of urban scenes.” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7892-7901, Apr 2018. doi: https://doi.org/10.48550/arXiv.1711.11556
Cordts, Marius, et al. “The cityscapes dataset for semantic urban scene understanding.” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213-3223, June 2016. doi: https://doi.org/10.1109/cvpr.2016.350
Brostow, Gabriel J., Julien Fauqueur, and Roberto Cipolla. “Semantic object classes in video: A high-definition ground truth database.” Pattern recognition letters 30.2, pp. 88-97, Jan 2009. doi: https://doi.org/10.1016/j.patrec.2008.04.005
Meletis, Panagiotis. “Towards holistic scene understanding: Semantic segmentation and beyond.” arXiv preprint arXiv:2201.07734, Jan 2022. doi: https://doi.org/10.48550/arXiv.2201.07734
Chen, Yuhua, Wen Li, and Luc Van Gool. “Road: Reality oriented adaptation for semantic segmentation of urban scenes.” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7892-7901, Apr 2018. doi: https://doi.org/10.48550/arXiv.1711.11556
Cordts, Marius, et al. “The cityscapes dataset for semantic urban scene understanding.” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213-3223, June 2016. doi: https://doi.org/10.1109/cvpr.2016.350
Brostow, Gabriel J., Julien Fauqueur, and Roberto Cipolla. “Semantic object classes in video: A high-definition ground truth database.” Pattern recognition letters 30.2, pp. 88-97, Jan 2009. doi: https://doi.org/10.1016/j.patrec.2008.04.005
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