EL-YOLO: An efficient and lightweight low-altitude aerial objects detector for onboard applications
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Integration with contextual AI frameworks, such as Vision Transformers, further enhances YOLO's ability to operate in dynamic environments.
“Integration with contextual AI frameworks, such as Vision Transformers, further enhances YOLO's ability to operate in dynamic environments [154].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [154]
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022) · arXiv · doi:10.48550/arxiv.2207.02696 · Cited by 13,205 · see citing works
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00721 · Cited by 13,198 · see citing works
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307 · see citing works
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841 · see citing works
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 · see citing works
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
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A Structured Self-attentive Sentence Embedding
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Detection and tracking meet drones challenge
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QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
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Intriguing Properties of Vision Transformers
Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Khan, F. S., & Yang, M.-H. (2021) · arXiv · doi:10.48550/arxiv.2105.10497 · Cited by 431 · see citing works
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Xue, C., Xia, Y., Wu, M., Chen, Z., Cheng, F., & Yun, L.
- Affiliations
- Yunnan Normal University (CN)
- Corresponding author
- Yun, Lijun
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 256
- Pages
- 124848
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4400966748
- Cited by
- 82
- References
- 11
- Citations by year
- 2026: 31 · 2025: 43 · 2024: 8
- FWCI
- 11.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- small object detection, lightweight object detection, onboard deployment, edge computing, feature pyramid network, multi-head self-attention, Jetson Xavier NX, unmanned aerial vehicles, YOLO
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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