基于3D卷积的图像序列特征提取与自注意力的车牌识别方法
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曾淦雄, 柯逍
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3D convolution-based image sequence feature extraction and self-attention for license plate recognition method
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Ganxiong ZENG, Xiao KE
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表2 不同方法在CCPD数据集上的车牌识别率
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| 方法 | All | Base | DB | FN | Rotate | Tilt | Weather | Challenge | | Faster RCNN[43]+HC | 92.8% | 97.2% | 94.4% | 90.9% | 82.9% | 87.3% | 85.5% | 76.3% | | YOLO9000[14]+HC | 93.7% | 98.1% | 96% | 88.2% | 84.5% | 88.5% | 87.0% | 80.5% | | SSD300[45]+HC | 95.2% | 98.3% | 96.6% | 95.9% | 88.4% | 91.5% | 87.3% | 83.8% | | TE2E[19] | 94.4% | 97.8% | 94.8% | 94.5% | 87.9% | 92.1% | 86.8% | 81.2% | | RPnet[7] | 95.5% | 98.5% | 96.9% | 94.3% | 90.8% | 92.5% | 87.9% | 85.1% | | DAN[21] | 96.6% | 98.9% | 96.1% | 96.4% | 91.9% | 93.7% | 95.4% | 83.1% | | MORAN[20] | 98.3% | 99.5% | 98.1% | 98.6% | 98.1% | 98.6% | 97.6% | 86.5% | | MTLPR[18] | 98.8% | - | - | - | - | - | - | - | | LPRNet[12] | 93.0% | 97.8% | 92.2% | 91.9% | 79.4% | 85.8% | 92.0% | 69.8% | | ANet (real data only)[11] | 98.5% | 99.6% | 98.8% | 98.8% | 96.4% | 97.6% | 98.5% | 88.9% | | ANet (real+synthetic data)[11] | 98.9% | | | 99.1% | 98.1% | | 98.6% | 89.7% | | T-LPR | | 99.6% | 99.1% | | | 98.6% | | |
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