This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic.
1165 Chinese plates from different provinces
Note: The license plates on the image were modified to protect the owners' privacy.
| Metric | Value |
|---|---|
| Rotation in-plane | ±10˚ |
| Rotation out-of-plane | Yaw: ±45˚ / Pitch: ±45˚ |
| Min plate width | 94 pixels |
| Ratio of correct reads | 88.58% |
| GFlops | 0.328 |
| MParams | 1.218 |
| Source framework | Caffe* |
Only "blue" license plates, which are common in public, were tested thoroughly. Other types of license plates may underperform.
- name: "data" , shape: [1x3x24x94] - An input image in following format [1xCxHxW]. Expected color order is BGR.
- name: "seq_ind" , shape: [88,1] - An auxiliary blob that is needed for correct decoding. Set this to
[0, 1, 1, ..., 1].
-
name: "decode", shape: [1, 88, 1, 1] - Encoded vector of floats. Each float is an integer number encoding a character according to this dictionary:
0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 <Anhui> 11 <Beijing> 12 <Chongqing> 13 <Fujian> 14 <Gansu> 15 <Guangdong> 16 <Guangxi> 17 <Guizhou> 18 <Hainan> 19 <Hebei> 20 <Heilongjiang> 21 <Henan> 22 <HongKong> 23 <Hubei> 24 <Hunan> 25 <InnerMongolia> 26 <Jiangsu> 27 <Jiangxi> 28 <Jilin> 29 <Liaoning> 30 <Macau> 31 <Ningxia> 32 <Qinghai> 33 <Shaanxi> 34 <Shandong> 35 <Shanghai> 36 <Shanxi> 37 <Sichuan> 38 <Tianjin> 39 <Tibet> 40 <Xinjiang> 41 <Yunnan> 42 <Zhejiang> 43 <police> 44 A 45 B 46 C 47 D 48 E 49 F 50 G 51 H 52 I 53 J 54 K 55 L 56 M 57 N 58 O 59 P 60 Q 61 R 62 S 63 T 64 U 65 V 66 W 67 X 68 Y 69 Z
[*] Other names and brands may be claimed as the property of others.