1 # Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
3 @page dwx_sign_classification_sample Traffic Sign Classification Sample (SignNet)
6 @note SW Release Applicability: This sample is available in **NVIDIA DRIVE Software** releases.
8 @section dwx_sign_classification_description Description
10 The Traffic Sign Classification sample demonstrates how to use the NVIDIA<sup>®</sup>
11 proprietary SignNet deep neural network (DNN) to perform traffic sign classification.
12 It outputs the class of the traffic signs from images captured by the ego car.
14 SignNet currently supports RCB images. RGBA images are not supported. SignNet models currently cover three geographical regions. For one of such regions, the United States (US), one model is provided: `US_V2` and `US_V4`. For another supported region, the European Union (EU), there are also two available models: `EU_V3` and `EU_V4`. For the last supported region, Japan, there is only one supported model - `JP_V1`. The main difference between different model versions for the EU and US regions is in the number of supported classes. Models version 2 for each region have significantly more traffic sign classes covered than version 1 models.
16 The default model for the sample app is `US_V2` for the US. In order to use the European traffic sign models, the Japan model, or to use the US model version 4, one would need to explicitly select it with a corresponding command line parameter when running the sample.
18 This sample shows a simple implementation of traffic sign classification built around the NVIDIA SignNet DNN. The classification is done by first detecting the traffic signs with the help of NVidia DriveNet DNN and then classification of found image-crops with the help of SignNet DNN. There is no tracking of traffic signs applied, so one may notice some flickering of detections.
19 For more information on the SignNet DNN and how to customize it for your applications, consult your NVIDIA sales or business representative.
21 @subsection dwx_sign_classification_sensor_details Sensor Details
23 The image datasets used to train SignNet have been captured by a View Sekonix Camera Module (SF3325) with
24 AR0231 RCCB sensor. The camera is mounted on a rig on top of the vehicle. Demo videos are
25 captured at 2.3 MP and downsampled to 960 x 604. Eight cameras were used to collect the data for training the provided SignNet models.
26 The following list shows the setup position and field of view (FOV) of each such camera:
27 - Center Front 60° FOV
28 - Center Front 120° FOV
29 - Center Front 30° FOV
30 - Center Right 120° FOV
31 - Center Left 120° FOV
32 - Rear Left 120° FOV
33 - Rear Center 120° FOV
34 - Rear Center 60° FOV
36 To achieve the best traffic sign detection performance, NVIDIA recommends to adopt a camera setup similar to one or more cameras from the list above and align the video center vertically with the horizon before recording new videos.
38 @subsection dwx_sign_classification_sample_limitations Limitations
40 @warning Currently, the SignNet DNN has limitations that could affect its performance:
41 - It was trained mostly for bright day-light, overcast, twilight, non-rain visibility conditions.
42 Training for artificial light, night-light conditions, and rainy-weather visibility was limited and, thus, the performance of the classifier may suffer in rain or in constrained illumination.
43 - The classification performance of SignNet depends on the size of the traffic signs detected in an image frame.
44 Good classification performance is observed when the height of the traffic signs is 20 pixels or more. Predictions for very small signs may be unreliable.
45 - The provided SignNet models were trained on data collected in the United States, Japan, and the countries comprising European Union.
46 As a result, SignNet models may not be suitable for other geographical regions. However, the use of the EU model may be appropriate for other countries which adopted the Vienna convention for traffic signs. But the specific sign classes available in those countries should be reviewed case by cases basis against the ones available in the provided model.
48 Even though the SignNet DNN was trained with data from cameras setup pointing in various direction of the sensor rig (see the list above), it is recommeded to use it for the following directional and FOV setup::
49 * Center-front camera location with a 60° FOV.
50 * Center-front camera location with a 120° FOV.
52 @section dwx_sign_classification_running Running the Sample
54 The command line for the sample is:
56 ./sample_sign_classifier --rig=[path/to/rig/file]
61 --rig=[path/to/rig/file]
62 Rig file containing all information about vehicle sensors and calibration.
63 Default value with video: path/to/data/samples/waitcondition/rig.json
64 Default value with live camera: path/to/data/samples/waitcondition/live_cam_rig.json
67 Use live camera or video file. Takes no effect on x86.
68 Need to be set to 1 if passing in a rig with live camera setup.
69 To switch the mode, pass `--liveCam=0/1` as the argument.
72 ### To run the sample on Linux
74 ./sample_sign_classifier
76 ### To run the sample on video with european signs classifier version 2:
78 ./sample_sign_classifier --model=EU_V2
80 ### To run the sample on a camera on NVIDIA DRIVE platforms
82 ./sample_sign_classifier --liveCam=1
84 @subsection dwx_sign_classification_output Output
86 The sample creates a window, displays a video, and overlays bounding boxes for detected traffic signs.
87 The class of the sign is displayed with the text label above the bounding box.
89 The following table describes the models provided as part of the package. Follow the hyperlinks to the the full list of classes
90 supported by each model.
92 | MODEL NAME | MODEL IDENTIFIER | SUPPORTED REGION | # OF MODEL'S OUTPUTS | # OF DISTINCT CLASS LABELS | DESCRIPTION |
93 |-------------------|------------------------|---------------------------|----------------------|----------------------------|----------------|
94 | @ref signnet_US_v2_0 | US_V2 | United States of America | 312 | 273 | Advanced USA model with expanded sign coverage.|
95 | TrafficSign_US_v4 | US_V4 | United States of America | 312 | 273 | Advanced USA model with expanded sign coverage and HWISP support. |
96 | signnet_EU_v3_0 | EU_V3 | European Union | 242 | 232 | Advanced EU model with expanded sign coverage. |
97 | TrafficSign_EU_v4 | EU_V4 | European Union | 242 | 232 | Advanced EU model with expanded sign coverage and HWISP. |
98 | @ref signnet_JP_v1_0 | JP_V1 | Japan | 184 | 151 | Japan sign model.
100 Note, the EU SignNet models may be appropriate to classify road signs from other non-EU countries that follow Vienna convention.
102 
104 @section dwx_sign_classification_more Additional Information
106 For more information, see @ref signnet_mainsection.