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
- 2022.9
- 수록면
- 811 - 816 (6page)
- DOI
- 10.5302/J.ICROS.2022.22.0102
이용수
초록· 키워드
오류제보하기
Target object recognition by a mobile robot is based on deep learning. To train a deep learning model to differentiate between target and non-target objects, a sufficiently large dataset that can extract features for classifying objects is required. In a general working environment, securing a sufficiently large dataset is difficult because the number of people designated as workers is constantly changing. Classifying the shapes of objects that are similar requires larger datasets than classifying the shapes of objects that are not similar. Therefore, a method for recognizing target objects with similar shapes using a small dataset is required. This paper proposes a Siamese-network–based target object recognition method for recognizing objects with similar shapes based on a small dataset. A trained Siamese network is used to recognize whether the input object is the target object based on its similarity to the target object. The results of target object recognition using the proposed method were experimentally analyzed. Further, ResNet-50 was used to evaluate the performance of the proposed method. Our findings show that the proposed method recognized objects with a difference of approximately 6% using a small dataset, indicating its higher efficiency than the classification-based object recognition method.
목차
Abstract
I. 서론
II. 유사한 형태의 특정 객체 인식을 위한 샴 네트워크
III. 실험
IV. 결론
REFERENCES
참고문헌 (16)
참고문헌 신청
H. Son, D. Kim, and S. Yang, “Development of following algorithm for human-following robots using deep-learning technology and depth camera,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no.2, pp. 95-101, Feb. 2022.
J. Noh, K. Yang, M. Park, J. Lee, M. Kim, and K. Seo, “LiDAR point cloud augmentation for mobile robot safe navigation in indoor environment,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 1, pp. 52-58, Jan. 2022.
J. Kim, D. Kim, and K. Koo, “Position recognition and driving control for an autonomous mobile robot that tracks tile grid pattern,” The Transactions of the Korean Institute of Electrical Engineers (in Korean), vol. 70, no. 6, pp. 945-952, Jun. 2021.
J. Hsieh, M. Chiang, C. Fang, and S. Chen, “Online human action recognition using deep learning for indoor smart mobile robots,” Proc. of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 425-433, 2021.
S. Secul and M. Ozkan, “Minimum distance calculation using skeletal tracking for safe human-robot interaction,” Robotics and Computer-Integrated Manufacturing, vol. 73, pp. 1-15, Sep. 2021.
J. Noh, K. Yang, M. Park, J. Lee, M. Kim, and K. Seo, “LiDAR point cloud augmentation for mobile robot safe navigation in indoor environment,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 1, pp. 52-58, Jan. 2022.
J. Kim, D. Kim, and K. Koo, “Position recognition and driving control for an autonomous mobile robot that tracks tile grid pattern,” The Transactions of the Korean Institute of Electrical Engineers (in Korean), vol. 70, no. 6, pp. 945-952, Jun. 2021.
J. Hsieh, M. Chiang, C. Fang, and S. Chen, “Online human action recognition using deep learning for indoor smart mobile robots,” Proc. of the 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 425-433, 2021.
S. Secul and M. Ozkan, “Minimum distance calculation using skeletal tracking for safe human-robot interaction,” Robotics and Computer-Integrated Manufacturing, vol. 73, pp. 1-15, Sep. 2021.
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