The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
RDD-YOLO: A modified YOLO for detection of steel surface defects
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Citations
Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
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
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
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
Cited by
DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects
Zhang, Y., Zhang, H., Huang, Q., Han, Y., & Zhao, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122669 · Cited by 291
Metadata
- Authors
- Zhao, C., Shu, X., Yan, X., Zuo, X., & Zhu, F.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 214
- Pages
- 112776
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W4360838197
- Cited by
- 386
- References
- 37
- FWCI
- 51.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: RDD-YOLO: A modified YOLO for detection of steel surface defects. https://app.cochat.ai/papers/10.1016/j.measurement.2023.112776
- The paper
- Zhao, C., Shu, X., Yan, X., Zuo, X., & Zhu, F. (2023). RDD-YOLO: A modified YOLO for detection of steel surface defects. Measurement. https://doi.org/10.1016/j.measurement.2023.112776
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.
