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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 565 - 571 (7page)
- DOI
- 10.5302/J.ICROS.2021.21.0059
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초록· 키워드
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This paper proposes a detection method for parking areas and collision risk areas in parking situations. Deep learning algorithms used for area detection rely on semantic segmentation, a method of classifying pixels into semantic segmentation. The main architecture of this paper is based on a harmonic densely connected network and a cross-stage partial network. The dataset was calibrated for four 190-degree wide-angle cameras to generate 500 AVM images based on the Chungbuk National University parking lot, and the experiment was performed with this dataset. In the experimental results, we found that the available parking area can be visualized by detecting the parking line, the parking area, and the available driving area in the AVM image. We visualized the area that remained undetected, as a collision risk area, through semantic segmentation to derive the results. According to the proposed CSPHarDNet model, the experimental results obtained were 81.89% mIoU and 18.36 FPS in the NVIDIA Xavier environment.
목차
Abstract
I. 서론
II. 관련 연구
III. 주차영역 및 충돌위험 영역 검출 알고리즘
IV. 실험
V. 결론
REFERENCES
참고문헌 (19)
참고문헌 신청
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017.
P. Chao, C.-Y. Kao, Y.-S. Ruan, C.-H. Huang, and Y.-L. Lin, “HarDNet: A low memory traffic network,” Proc. of IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “CSPNet: A new backbone that can enhance learning capability of cnn,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshop), 2020.
W. Li, L. Cao, and L. Yan, “Vacant parking slot detection in the around view image based on deep learning,” MDPI Sensors, 2020.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” Proc. of International Conference on Learning Representations (ICLR), May 2016.
P. Chao, C.-Y. Kao, Y.-S. Ruan, C.-H. Huang, and Y.-L. Lin, “HarDNet: A low memory traffic network,” Proc. of IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “CSPNet: A new backbone that can enhance learning capability of cnn,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR Workshop), 2020.
W. Li, L. Cao, and L. Yan, “Vacant parking slot detection in the around view image based on deep learning,” MDPI Sensors, 2020.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” Proc. of International Conference on Learning Representations (ICLR), May 2016.
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UCI(KEPA) : I410-ECN-0101-2021-003-001917578