Light-YOLOv3: License Plate Detection in Multi-Vehicle Scenario

Sun, Yuchao; Qiao, Peng; Zhang, Dengyin

doi:10.1587/transinf.2020edp7260

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Slimani et al [31] based on wavelet transform for license plate detection, followed by validation of potential regions using CNN classifier. Another category is end-to-end detection algorithms, which directly get the location coordinates and class probability of the target, typical algorithms are SSD [32], YOLO [33], [34], [35]. The form1er has a lower recognition speed and the latter is slightly less accurate.…”

Section: Related Workmentioning

confidence: 99%

License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi

Zhao

2023

IEEE Access

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Aiming at the problem that the traditional license plate recognition method lacking of accuracy and speed, an end-to-end deep learning model for license plate location and recognition in natural scenarios was proposed. First, we added an improved channel attention mechanism to the down-sampling process of the You only look once(YOLOv5). Additionally, a location information is added in the ones to minimize the information loss from sampling, which can improve the feature extraction ability of the model. Then we reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Finally, Gated recurrent units(GRU) + Connectionist temporal classification(CTC) was used to build the recognition network to complete the character segmentation-free recognition task of the license plate, significantly shortened the training time and improved the convergence speed and recognition accuracy of the network. The experimental results show that the average recognition precision of the license plate recognition model proposed in this paper reaches 98.98%, which is significantly better than the traditional recognition algorithm, and the recognition effect is good in complex environment with good stability and robustness.

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Section: Related Workmentioning

confidence: 99%

License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi

Zhao

2023

IEEE Access

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“…The representative algorithms are R-CNN [1] frameworks. One-stage network directly converts the problem of target frame positioning into regression problem processing, which greatly improves the speed of image detection, while also providing good accuracy [26]. Representative algorithms include SSD [10], Yolov1 [11], Yolov2 [12], Yolov3 [13], and Yolov4 [14].…”

Section: Related Workmentioning

confidence: 99%

“…Representative algorithms include SSD [10], Yolov1 [11], Yolov2 [12], Yolov3 [13], and Yolov4 [14]. SUN et al [26] present a faster and lightweight YOLOv3 model for multi-vehicle or under-illuminated images scenario. Zhang et al present RefineDet [15], one of the latest single-stage object detectors, to demonstrate the generality of their proposal.…”

Section: Related Workmentioning

confidence: 99%

BFF R-CNN: Balanced Feature Fusion for Object Detection

Liu

WANG

et al. 2022

IEICE Trans. Inf. & Syst.

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In the neck part of a two-stage object detection network, feature fusion is generally carried out in either a top-down or bottom-up manner. However, two types of imbalance may exist: feature imbalance in the neck of the model and gradient imbalance in the region of interest extraction layer due to the scale changes of objects. The deeper the network is, the more abstract the learned features are, that is to say, more semantic information can be extracted. However, the extracted image background, spatial location, and other resolution information are less. In contrast, the shallow part can learn little semantic information, but a lot of spatial location information. We propose the Both Ends to Centre to Multiple Layers (BEtM) feature fusion method to solve the feature imbalance problem in the neck and a Multi-level Region of Interest Feature Extraction (MRoIE) layer to solve the gradient imbalance problem. In combination with the Regionbased Convolutional Neural Network (R-CNN) framework, our Balanced Feature Fusion (BFF) method offers significantly improved network performance compared with the Faster R-CNN architecture. On the MS COCO 2017 dataset, it achieves an average precision (AP) that is 1.9 points and 3.2 points higher than those of the Feature Pyramid Network (FPN) Faster R-CNN framework and the Generic Region of Interest Extractor (GRoIE) framework, respectively.

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Design of License Plate Recognition System Based on OpenCV

Duan

Luo

2022

2022 2nd International Conference on Networking Systems of AI (INSAI)

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Light-YOLOv3: License Plate Detection in Multi-Vehicle Scenario

Cited by 3 publications

References 13 publications

License Plate Recognition System Based on Improved YOLOv5 and GRU

License Plate Recognition System Based on Improved YOLOv5 and GRU

BFF R-CNN: Balanced Feature Fusion for Object Detection

Design of License Plate Recognition System Based on OpenCV

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