The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system
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Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(2016) · Cited by 31,590
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
Cited by
A Variational Framework for Underwater Image Dehazing and Deblurring
Xie, J., Hou, G., Wang, G., & Pan, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3115791 · Cited by 259
Survey of deep learning for autonomous surface vehicles in marine environments
Qiao, Y., Yin, J., Wang, W., Duarte, F., Yang, J., & Ratti, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235911 · Cited by 192
Prediction of $$\beta $$-Thalassemia carriers using complete blood count features
Rustam, F., Ashraf, I., Jabbar, S., Tutusaus, K., Mazas, C., Barrera, A. E. P., & de la Torre Diez, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-22011-8 · Cited by 28
Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors
Zhou, T., Hu, Q., Hu, Z., & Zhi, J. (2022) · Journal of Advanced Transportation · doi:10.1155/2022/5333171 · Cited by 6
Metadata
- Authors
- Liu, R. W., Yuan, W., Chen, X., & Lu, Y.
- Venue
- Ocean Engineering
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 235
- Pages
- 109435
- ISSN
- 0029-8018 (ISSN-L)
- OpenAlex
- W3181175604
- Cited by
- 269
- References
- 67
- FWCI
- 16.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system. https://app.cochat.ai/papers/10.1016/j.oceaneng.2021.109435
- The paper
- Liu, R. W., Yuan, W., Chen, X., & Lu, Y. (2021). An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system. Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2021.109435
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
