license-plate-recognition-barrier-0001

Use Case and High-Level Description

This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic.

Validation Dataset - Internal

1165 Chinese plates from different provinces

Example

Note: The license plates on the image were modified to protect the owners’ privacy.

_images/license-plate-recognition-barrier-0001.png

Specification

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*

Limitations

Only “blue” license plates, which are common in public, were tested thoroughly. Other types of license plates may underperform.

Inputs

  1. Image, name: data, shape: 1, 3, 24, 94 in the format 1, C, H, W, where:

    • C - number of channels

    • H - image height

    • W - image width

    Expected color order is BGR.

  2. An auxiliary blob that is needed for correct decoding, name: seq_ind, shape: 88,1. Set this to [1, 1, 1, ..., 1].

Outputs

Encoded vector of floats, name: decode, shape: 1, 88, 1, 1. 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

Use Case and High-Level Description

This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic.

Validation Dataset - Internal

1165 Chinese plates from different provinces

Example

Note: The license plates on the image were modified to protect the owners’ privacy.

_images/license-plate-recognition-barrier-0001.png

Specification

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*

Limitations

Only “blue” license plates, which are common in public, were tested thoroughly. Other types of license plates may underperform.

Inputs

  1. Image, name: data, shape: 1, 3, 24, 94 in the format 1, C, H, W, where:

    • C - number of channels

    • H - image height

    • W - image width

    Expected color order is BGR.

  2. An auxiliary blob that is needed for correct decoding, name: seq_ind, shape: 88,1. Set this to [1, 1, 1, ..., 1].

Outputs

Encoded vector of floats, name: decode, shape: 1, 88, 1, 1. 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

Legal Information

[*] Other names and brands may be claimed as the property of others.