The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset
Open accessAbstract
SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located bounding boxes in the ground truth of the SMD. In this paper, for the benchmark of DNN algorithms, we correct the annotations of the SMD dataset and present an improved version, which we coined SMD-Plus. We also propose augmentation techniques designed especially for the SMD-Plus. More specifically, an online transformation of training images via Copy & Paste is applied to solve the class-imbalance problem in the training dataset. Furthermore, the mix-up technique is adopted in addition to the basic augmentation techniques for YOLO-V5. Experimental results show that the detection and classification performance of the modified YOLO-V5 with the SMD-Plus has improved in comparison to the original YOLO-V5. The ground truth of the SMD-Plus and our experimental results are available for download.
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Cites
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
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
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z., Shen, C., Chen, H., & He, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00972 · Cited by 7,013
Cited by
Kumar, S., Singh, S. K., Varshney, S., Singh, S., Kumar, P., Kim, B.-G., & Ra, I.-H. (2023) · Sustainability · doi:10.3390/su152416869 · Cited by 46
Metadata
- Authors
- Kim, J.-H., Kim, N., Park, Y. W., & Won, C. S.
- Venue
- Journal of Marine Science and Engineering
- Publisher
- MDPI AG
- Published
- 2022-03-06
- Volume
- 10
- Issue
- 3
- Pages
- 377
- ISSN
- 2077-1312 (ISSN-L)
- OpenAlex
- W4220681577
- Cited by
- 252
- References
- 5
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kim, J.-H., Kim, N., Park, Y. W., & Won, C. S. (2022). Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset. Journal of Marine Science and Engineering. https://doi.org/10.3390/jmse10030377
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