The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A Review of Yolo Algorithm Developments
Open accessAbstract
Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature.
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“Although several papers have reviewed YOLO architectures, they often exhibit limitations such as focusing on specific YOLO variants [10], [11], or concentrating on particular application domains [12].”
From YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024) · cites this paper as [10]
Citations
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
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
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Laroca, R., Severo, E. B., Junior, L. A. Z., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., & Menotti, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489629 · Cited by 670
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
Huang, R., Pedoeem, J., & Chen, C. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621865 · Cited by 622
A Review of Object Detection Models Based on Convolutional Neural Network
Sultana, F., Sufian, A., & Dutta, P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4288-6_1 · Cited by 327
A review of object detection based on convolutional neural network
Wang, Z., & Liu, J. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8029130 · Cited by 268
A YOLO-Based Traffic Counting System
Lin, J., & Sun, M.-T. (2018) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai.2018.00027 · Cited by 116
A Vehicle Detection Method for Aerial Image Based on YOLO
Lu, J., Ma, C., Li, L., Xing, X., Zhang, Y., Wang, Z., & Xu, J. (2018) · Journal of Computer and Communications · doi:10.4236/jcc.2018.611009 · Cited by 101
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Sliding window regression method for mechanical meter reading recognition in time-series images
Xiu, H., Hu, S., Cui, Y., He, J., Zhang, X., & Qi, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111240 · Cited by 4
Metadata
- Authors
- Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B.
- Affiliations
- Southwest Minzu University (CN)
- Corresponding author
- Ergu, Daji
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 199
- Pages
- 1066-1073
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4210598935
- Cited by
- 2,783
- References
- 15
- Citations by year
- 2026: 474 · 2025: 880 · 2024: 879 · 2023: 464 · 2022: 86
- FWCI
- 209.36
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Funders
- Fundamental Research Funds for the Central Universities
- Southwest Minzu University (2018NZD02, 2020NGD04)
- National Natural Science Foundation of China (71774134, U1811462)
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- YOLO, object detection, convolutional neural networks, feature selection, feature extraction, target recognition
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