The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Real-Time Vehicle Detection Based on Improved YOLO v5
Open accessAbstract
To reduce the false detection rate of vehicle targets caused by occlusion, an improved method of vehicle detection in different traffic scenarios based on an improved YOLO v5 network is proposed. The proposed method uses the Flip-Mosaic algorithm to enhance the network’s perception of small targets. A multi-type vehicle target dataset collected in different scenarios was set up. The detection model was trained based on the dataset. The experimental results showed that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.
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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
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
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2019) · arXiv · doi:10.48550/arxiv.1911.08287 · Cited by 5,313
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6999 · Cited by 5,288
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
Dong, Z., Wu, Y., Pei, M., & Jia, Y. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2402438 · Cited by 387 · cited 1×
“A total of 1493 pictures with vehicle information were extracted, and the original vehicle detection image library was set with the selection of 1060 pictures of multiple types of vehicles from the BIT Vehicle_Dataset [33].”
Method
Metadata
- Authors
- Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., & Guo, X.
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2022-09-27
- Volume
- 14
- Issue
- 19
- Pages
- 12274
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4298004595
- Cited by
- 454
- References
- 7
- FWCI
- 30.03
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhang, Y., Guo, Z., Wu, J., Tian, Y., Tang, H., & Guo, X. (2022). Real-Time Vehicle Detection Based on Improved YOLO v5. Sustainability. https://doi.org/10.3390/su141912274
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