基于单字符注意力的全品类鲁棒车牌识别

穆世义; 徐树公

doi:10.16383/j.aas.c211210

基于单字符注意力的全品类鲁棒车牌识别

doi: 10.16383/j.aas.c211210

穆世义^{1, 2,},
徐树公^{1, 2,}

1.
上海大学通信与信息工程学院上海 200444
2.
上海先进通信与数据科学研究院上海 200444

基金项目: 国家自然科学基金(61871262)资助

详细信息

作者简介:
穆世义：上海大学通信与信息工程学院硕士研究生. 主要研究方向为深度学习, 计算机视觉和光学字符识别. E-mail: mushiyishu@shu.edu.cn

徐树公：上海大学通信与信息工程学院教授. 主要研究方向为无线通信和模式识别. 本文通信作者. E-mail: shugong@shu.edu.cn

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出版历程
- 收稿日期: 2021-12-20
- 录用日期: 2022-04-07
- 网络出版日期: 2022-05-06
- 刊出日期: 2023-01-07

Full-category Robust License Plate Recognition Based on Character Attention

MU Shi-Yi^{1, 2
,},
XU Shu-Gong^{1, 2
,}

1.
School of Communication and Information Engineering, Shanghai University, Shanghai 200444
2.
Shanghai Institute for Advanced Communication and Data Science, Shanghai 200444

Funds: Supported by National Natural Science Foundation of China (61871262)

More Information

Author Bio:
MU Shi-Yi　Master student at the School of Communication and Information Engineering, Shanghai University. His research interest covers deep learning, computer vision and optical character recognition

XU Shu-Gong　Professor at the School of Communication and Info-rmation Engineering, Shanghai University. His research interest covers wireless communication and pattern recognition. Corresponding author of this paper

摘要

摘要: 复杂场景下的高精度车牌识别仍然存在着许多挑战, 除了光照、分辨率不可控和运动模糊等因素导致的车牌图像质量低之外, 还包括车牌品类多样产生的行数不一和字数不一等困难, 以及因拍摄角度多样出现的大倾角等问题. 针对这些挑战, 提出了一种基于单字符注意力的场景鲁棒的高精度车牌识别算法, 在无单字符位置标签信息的情况下, 使用注意力机制对车牌全局特征图进行单字符级特征分割, 以处理多品类车牌和倾斜车牌中的二维字符布局问题. 另外, 该算法通过使用共享参数的多分支结构代替现有算法的串行解码结构, 降低了分类头参数量并实现了并行化推理. 实验结果表明, 该算法在公开车牌数据集上实现了超越现有算法的精度, 同时具有较快的识别速度.
- 车牌识别 /
- 注意力机制 /
- 字符分割 /
- 字符分类
Abstract: There are still many challenges for high-precision vehicle license plate recognition in complex scenarios. In addition to the low quality of license plate images caused by factors such as poor illumination, low resolution, and motion blur, challenges also include different variant numbers of characters and lines for different license plate categories, as well as large inclination caused by the various camera locations. In response to these challenges, this paper proposes a scene-robust high-precision license plate recognition algorithm based on character attention, which performs character level segmentation on the global feature map of the license plate images without character position label information. Such character level segmentation can deal with the 2D character layout problems in multi-category license plates and inclined license plates. In addition, this algorithm uses a shared weight classification header structure to replace the serial decoding structure used in existing algorithms, which reduces the number of classification header parameters and realizes parallel inference. The experimental results show that the algorithm achieves high accuracy which surpasses the existing algorithms on the public-domain data sets, and meanwhile has a faster recognition speed.
- License plate recognition /
- attention mechanism /
- character segmentation /
- character classification

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图 1 注意力机制改进

Fig. 1 Evolution of attention mechanism

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图 2 CARNet算法结构图

Fig. 2 Framework of the proposed algorithm CARNet

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图 3 轻量化特征提取

Fig. 3 Lightweight feature extraction

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图 4 单字符注意力网络

Fig. 4 Single character attention network

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图 5 单字符特征分割

Fig. 5 Single character feature segmentation

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图 6 脚本生成的车牌样本

Fig. 6 License plate samples generated by script

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图 7 常见7字符车牌注意力图

Fig. 7 Attention maps of seven-character license plates

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图 8 双行黄牌、新能源车牌及黑色车牌注意力图

Fig. 8 Attention maps of double-line and new energy and black plate licenses

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图 9 真实复杂场景下的检测识别测试

Fig. 9 Detection and recognition test in complex scene

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图 10 识别错误示例

Fig. 10 Recognition error cases

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表 1 在CCPD上的车牌识别准确率(%)

Table 1 License plate recognition accuracy on CCPD (%)

算法	平均	基础集	明暗集	远近集	旋转集	倾斜集	天气集	挑战集
Li 等^[1]	94.4	97.8	94.8	94.5	87.9	92.1	86.8	81.2
Xu 等^[27]	95.5	98.5	96.9	94.3	90.8	92.5	87.9	85.1
Wang 等^[23]	96.6	98.9	96.1	96.4	91.9	93.7	95.4	83.1
Zou 等^[8]	97.8	99.3	98.5	98.6	92.5	94.4	99.3	86.6
Yang 等^[4]	97.5	99.1	96.9	95.9	97.1	98.0	97.5	85.9
Qin 等^[33]	97.5	99.5	93.3	93.7	98.2	95.9	98.9	92.9
Qiao 等^[34]	96.9	99.0	97.1	95.5	95.0	96.5	95.9	83.1
Zhang 等^[20]	98.5	99.6	98.8	98.8	96.4	97.6	98.5	88.9
Liu 等^[35]	98.74	99.73	99.05	99.23	97.62	98.40	98.89	88.51
GCN	98.79	99.70	99.07	98.96	98.33	98.82	98.66	89.42
CARNet	99.50 (0.02)	99.89 (0.01)	99.57 (0.08)	99.56 (0.04)	99.68 (0.04)	99.80 (0.01)	99.38 (0.06)	94.92 (0.09)

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表 2 本文算法有效性评估(%)

Table 2 Evaluation of the effectiveness of the algorithm of this paper (%)

评估指标	算法	平均	基础集	明暗集	远近集	旋转集	倾斜集	天气集	挑战集
$ {R_{LP}} $	GCN	98.79	99.70	99.07	98.96	98.33	98.82	98.66	89.42
	GCN	(0.10)	(0.03)	(0.11)	(0.11)	(0.25)	(0.19)	(0.13)	(0.54)
	CARNet	99.50	99.89	99.57	99.56	99.68	99.80	99.38	94.92
	CARNet	(0.02)	(0.01)	(0.08)	(0.04)	(0.04)	(0.01)	(0.06)	(0.09)
$ {R_{Char}} $	GCN	99.74	99.95	99.83	99.79	99.68	99.78	99.77	97.28
	GCN	(0.02)	(0.01)	(0.02)	(0.01)	(0.05)	(0.03)	(0.02)	(0.14)
	CARNet	99.90	99.98	99.94	99.93	99.95	99.97	99.90	98.89
	CARNet	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
$ {R_{C\_Char}} $	GCN	99.72	99.87	99.78	99.78	99.56	99.71	99.70	98.18
	GCN	(0.02)	(0.01)	(0.02)	(0.01)	(0.06)	(0.03)	(0.06)	(0.07)
	CARNet	99.92	99.99	99.93	99.89	99.95	99.98	99.95	99.13
	CARNet	(0.01)	(0.01)	(0.02)	(0.02)	(0.01)	(0.01)	(0.02)	(0.01)
$ {R_{W\_Char}} $	GCN	99.74	99.97	99.83	99.80	99.70	99.80	99.78	97.13
	GCN	(0.02)	(0.01)	(0.02)	(0.01)	(0.05)	(0.03)	(0.01)	(0.16)
	CARNet	99.90	99.98	99.94	99.93	99.95	99.97	99.89	98.85
	CARNet	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)

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表 3 在CLPD上的车牌识别准确率

Table 3 License plate recognition accuracy on CLPD

算法	训练数据	${R_{LP} }$(%)
Zhang 等^[20]	CCPD 基础集	70.8
GCN^[22]	CCPD 基础集	74.4
Zou 等^[8]	CCPD 基础集	80.7
Liu 等^[35]	CCPD 基础集	80.3
CARNet	CCPD 基础集	82.2
CARNet	混合数据集	83.9

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表 4 在混合品类车牌上的识别准确率

Table 4 Recognition accuracy on mixed types of license plates

车牌类别	数量 (张)	GCN^[22] (%)	CARNet (%)
蓝牌车牌	1050	96.3	99.0
新能源绿牌	1010	41.9	78.5
大型车后牌	660	35.8	62.1
教练车牌	860	44.9	74.8
港澳车牌	1580	53.7	74.9
大型车前牌	980	63.2	75.8

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表 5 各算法速度比较

Table 5 Comparison of algorithm speed

车牌识别算法	车牌识别耗时 (ms)
Zhang 等^[20]	7.9
GCN^[22]	18.7
CARNet	4.9

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表 6 低功耗嵌入式硬件测试

Table 6 Low-power embedded device test

算法	硬件平台	推理引擎	耗时 (ms)
Qin 等^[9]	Jetson Nano	TensorFlow	68
CARNet	Jetson Nano	Pytorch	41
CARNet	Jetson TX2	Pytorch	30
CARNet	Hi3516DV300	NNIE	46

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表 7 特征提取网络消融实验

Table 7 Feature extraction ablation experiment

特征提取	$ {R_{LP}} $(%)	参数量	计算复杂度 (GMacs)
Resnet45^[25]	99.5	13.94 M	14.66
Xception19	99.5	1.87 M	1.71

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表 8 分类头参数共享消融实验

Table 8 Classification head weight sharing ablation experiment

参数共享	$ {R_{LP}} $(%)	参数量	计算复杂度 (GMacs)
是	99.5	1.87 M	1.71
否	99.4	3.82 M	1.71

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表 9 单字符注意力消融实验

Table 9 Ablation experiments for single-character attention

单字符注意力	$ {R_{LP}} $(%)	参数量	计算复杂度 (GMacs)
有	99.5	1.87 M	1.71
无	99.1	3.04 M	1.02

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