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Owing to the development of computer vision technology, much effort is being conducted to apply it in the maritime field. In this study, we developed a model that can detect various types of ships using object detection. Nine types of ship images were downloaded, and bounding box processing of the ships in the images was performed. Among the You Only Look Once (YOLO) model versions for object detection, YOLO v3 and YOLO v5s were used to train the training set, and predictions were made on the validation and testing sets. For the validation and testing sets, both models made good predictions. However, as some mispredictions occurred in the testing set, recommendations for these are given in the last section.

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기사명 저자명 페이지 원문 목차
(A) novel proposal for a marine fuel cell system utilizes LNG as a sustainable and green fuel for the future of shipping Nguyen Quoc Huy, Phan Anh Duong, Tran The Nam, To Thi Thu Ha, Bo Rim Ryu, Hokeun Kang p. 46-54

Comparative assessment of the effects of higher alcohols/diesel blends on performance and emissions of a direct-injection diesel engine Cheol-oh Park, Jeonghyeon Yang, Jaesung Kwon p. 55-62

Performance analysis of a constant-speed LPG engine Ki-young Han, Hyo-geun Lim, Ji-woong Lee p. 63-71

Influence of nonlinear DC electric fields on premixed flame characteristics Dae Won Im, Sung Hwan Yoon p. 72-80

Object detection for various types of vessels using the YOLO algorithm Min-Ho Park, Jae-Hyuk Choi, Won-Ju Lee p. 81-88

Analysis of implantable catheter for draining malignant ascites Inwoo Kim, Hyeonjong Kim, Il-Hwan Kim, Junghyuk Ko p. 89-95

Direct orientation estimation through inertial odometry based on a deep transformer model Yoon-Sang Han, Min-Jae Kim, Hong-Il Seo, Dong-Hoan Seo p. 96-106

참고문헌 (21건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Y. Dong, F. Chen, S. Han, and H. Liu, “Ship object detec-tion of remote sensing image based on visual attention,” Remote Sensing, vol. 13, no. 16, p. 3192, 2021. doi:10.3390/rs13163192. 미소장
2 Y. Wang, C. Wang, H. Zhang, Y. Dong, and S. Wei, “Au-tomatic ship detection based on RetinaNet using multi-res-olution Gaofen-3 imagery,” Remote Sensing, vol. 11, no. 5, 2019. doi:10.3390/rs11050531. 미소장
3 R. Yang, Z. Pan, X. Jia, L. Zhang, and Y. Deng, “A novel CNN-based detector for ship detection based on rotatable bounding box in SAR images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1938-1958, 2021. doi:10.1109/JSTARS.2021.3049851. 미소장
4 J. Li, C. Qu, and J. Shao, “Ship detection in SAR images based on an improved faster R-CNN,” 2017 SAR in Big Data Era: Models, Methods and Applications, BIGSAR-DATA, pp. 1-6, 2017. doi:10.1109/BIGSAR-DATA.2017.8124934. 미소장
5 Y. L. Chang, A. Anagaw, L. Chang, Y. C. Wang, C. Y. Hsiao, and W. H. Lee, “Ship detection based on YOLOv2 for SAR imagery,” Remote Sensing, vol. 11, no. 7, 2019. doi:10.3390/rs11070786. 미소장
6 Z. Hong, T. Yang, X. Tong, et al., “Multi-scale ship detec-tion from SAR and optical imagery via a more accurate YOLOv3,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6083-6101, 2021. doi:10.1109/JSTARS.2021.3087555. 미소장
7 Z. Shao, H. Lyu, Y. Yin, et al., “Multi-scale object detection model for autonomous ship navigation in maritime environ-ment,” Journal of Marine Science and Engineering, vol. 10, no. 11, p. 1783, 2022. doi:10.3390/jmse10111783. 미소장
8 S. J. Lee, M. I. Roh, H. W. Lee, J. S. Ha, and I. G. Woo, “Image-based ship detection and classification for un-manned surface vehicle using real-time object detection neural networks,” International Offshore and Polar Engi-neering Conference, p. ISOPE-I-18-411, 2018. 미소장
9 Z. Shao, L. Wang, Z. Wang, W. Du, and W. Wu, “Saliency-aware convolution neural network for ship detection in sur-veillance video,” IEEE Transactions on Circuits and Sys-tems for Video Technology, vol. 30, no. 3, pp. 781-794, 2020. doi:10.1109/TCSVT.2019.2897980. 미소장
10 H. Li, L. Deng, C. Yang, J. Liu, and Z. Gu, “Enhanced YOLO v3 tiny network for real-time ship detection from visual image,” IEEE Access, vol. 9, pp. 16692-16706, 2021. doi:10.1109/ACCESS.2021.3053956. 미소장
11 J. H. Kim, N. Kim, Y. W. Park, and C. S. Won, “Object detection and classification based on YOLO-V5 with im-proved maritime dataset,” Journal of Marine Science and Engineering, vol. 10, no. 3, p. 377, 2022. doi:10.3390/jmse10030377. 미소장
12 Make Sense, Make Sense AI, https://www.makesense.ai/, Published 2024. 미소장
13 WIKIPEDIA, YAML, https://en.wikipe-dia.org/wiki/YAML, Published 2024. 미소장
14 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Pro-ceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016. doi:10.1109/CVPR.2016.91. 미소장
15 A. Kuznetsova, T. Maleva, V. Soloviev, “Detecting apples in orchards using YOLOv3 and YOLOv5 in general and close-up images,” Lecture Notes in Computer Science (In-cluding Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12557, 2020. doi:10.1007/978-3-030-64221-1_20. 미소장
16 A. Kuznetsova, T. Maleva, and V. Soloviev, “YOLOv5 ver-sus YOLOv3 for apple detection, Cyber-Physical Systems: Modelling and Intelligent Control. Studies in Systems, De-cision and Control, vol. 338, 2021. doi:10.1007/978-3-030-66077-2_28. 미소장
17 A. Khalfaoui, A. Badri, and I. EL Mourabit, “Comparative study of YOLOv3 and YOLOv5’s performances for real-time person detection,” 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, pp. 1-5, 2022. doi:10.1109/IRA-SET52964.2022.9737924. 미소장
18 J. Redmon and A. Farhadi, Yolov3: An incremental im-provement. arXiv Prepr arXiv180402767, 2018. 미소장
19 Y. Dai, W. Liu, H. Li, and L. Liu, “Efficient foreign object detection between PSDs and metro doors via deep neural networks. IEEE Access, vol. 8, pp. 46723-46734, 2020. doi:10.1109/ACCESS.2020.2978912. 미소장
20 Ultralytics, YOLO v5, https://github.com/ultralyt-ics/yolov5, Published 2020. 미소장
21 D. Dlužnevskij, P. Stefanovč, and S. Ramanauskaite, Inves-tigation of YOLOv5 efficiency in IPhone supported sys-tems, Baltic Journal of Modern Computing, vol. 9, no. 3, pp. 333-344, 2021. doi:10.22364/bjmc.2021.9.3.07. 미소장

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