The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning
Abstract
Abstract Automatic License Plate Recognition (ALPR) is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look Once (YOLO)-darknet deep learning framework. In this paper, we use YOLO's 7 convolutional layers to detect a single class. The detection method is a sliding-window process. The object is to recognize Taiwan's car license plates. We use an AOLP dataset which contained 6 digit car license plates. The sliding window detects each digit of the license plate, and each window is then detected by a single YOLO framework. The system achieves approximately 98.22% accuracy on license plate detection and 78% accuracy on license plate recognition. The system executes a single detection recognition phase, which needs around 800 ms to 1 s for each input image. The system is also tested with different condition complexities, such as rainy background, darkness and dimness, and different hues and saturation of images.
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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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2015) · doi:10.1109/cvpr31182.2015 · Cited by 3,737
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2001)
Hillman, P., Hannah, J. M., & Renshaw, D. (2001) · Cited by 1,998
An Analysis Of Convolutional Neural Networks For Image Classification
Sharma, N., Jain, V., & Mishra, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.198 · Cited by 701
License Plate Recognition From Still Images and Video Sequences: A Survey
Anagnostopoulos, C.-N. E., Anagnostopoulos, I. E., Psoroulas, I. D., Loumos, V., & Kayafas, E. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.922938 · Cited by 645
A new deep convolutional neural network for fast hyperspectral image classification
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.11.021 · Cited by 607
Diverse Region-Based CNN for Hyperspectral Image Classification
Zhang, M., Li, W., & Du, Q. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2809606 · Cited by 604
Deep convolutional neural networks for diabetic retinopathy detection by image classification
Wan, S., Liang, Y., & Zhang, Y. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.07.042 · Cited by 484
Cited by
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
Hussain, M. (2024) · IEEE Access · doi:10.1109/access.2024.3378568 · Cited by 492
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection
Xu, D., & Wu, Y. (2020) · Sensors · doi:10.3390/s20154276 · Cited by 141
Metadata
- Authors
- Hendry, & Chen, R.-C.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 87
- Pages
- 47-56
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2944525610
- Cited by
- 304
- References
- 31
- FWCI
- 27.09
- Type
- journal article
- Language
- en
- Open access
- closed
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