DocumentAIKit/deploy/pipeline/docs/tutorials/ppvehicle_retrograde_en.md

English | 简体中文

PP-Vehicle vehicle retrograde identification module

Vehicle reverse identification is widely used in smart cities, smart transportation and other directions. In PP-Vehicle, a vehicle retrograde identification module is integrated to identify whether the vehicle is retrograde.

task	algorithm	precision	infer speed	download
Vehicle detection/tracking	PP-YOLOE	mAP 63.9	38.67ms	infer deploy model
Lane line segmentation	PP-liteseg	mIou 32.69	47 ms	infer deploy model

Notes：

1. The prediction speed of vehicle detection/tracking model is based on NVIDIA T4 and TensorRT FP16. The model prediction speed includes data preprocessing, model prediction and post-processing.
2. The training and precision test of vehicle detection/tracking model are based on VeRi.
3. The predicted speed of lane line segmentation model is based on Tesla P40 and python prediction. The predicted speed of the model includes data preprocessing, model prediction and post-processing.
4. Lane line model training and precision testing are based on BDD100K-LaneSeg and Apollo Scape,The label data of the two data sets is in Lane_dataset_label

Instructions

Description of Configuration

[The parameters related to vehicle retrograde in config file is as follows:

LANE_SEG:
  lane_seg_config: deploy/pipeline/config/lane_seg_config.yml #lane line seg config file
  model_dir: https://bj.bcebos.com/v1/paddledet/models/pipeline/pp_lite_stdc2_bdd100k.zip   #model path

VEHICLE_RETROGRADE:
  frame_len: 8                        #Number of sampling frames
  sample_freq: 7                      #sampling frequency
  enable: True                        #Whether to enable the funcion
  filter_horizontal_flag: False       #Whether to filter vehicles in horizontal direction
  keep_right_flag: True               #According to the right driving rule, if the vehicle keeps left driving, it is set as False
  deviation: 23                       #Filter the horizontal angle vehicles threshold. If it is greater than this angle, filter
  move_scale: 0.01                    #Filter the threshold value of stationary vehicles. If the vehicle moving pixel is greater than the image diagonal * move_scale, the vehicle is considered moving, otherwise, the vehicle is stationary
  fence_line: []                      #Lane centerline coordinates, format[x1,y1,x2,y2] and y2>y1. If it is empty, the program will automatically judge according to the direction of traffic flow

The parameters related to Lane line segmentation in lane line seg config fileis as follows:

type: PLSLaneseg  #Select segmentation Model

PLSLaneseg:
  batch_size: 1                                       #image batch_size
  device: gpu                                         #device is gpu or cpu
  filter_flag: True                                   #Whether to filter the horizontal direction road route
  horizontal_filtration_degree: 23                    #Filter the threshold value of the lane line in the horizontal direction. When the difference between the maximum inclination angle and the minimum inclination angle of the segmented lane line is less than the threshold value, no filtering is performed

  horizontal_filtering_threshold: 0.25                #Determine the threshold value for separating the vertical direction from the horizontal direction thr=(min_degree+max_degree) * 0.25 Divide the lane line into vertical direction and horizontal direction according to the comparison between the gradient angle of the lane line and thr

How to Use

1. Download 'vehicle detection/tracking' and 'lane line recognition' two prediction deployment models from the model base and unzip them to '/ output_ Invitation ` under the path; By default, the model will be downloaded automatically. If you download it manually, you need to modify the model folder as the model storage path.

2. Modify ProfileVEHICLE_RETROGRADE-enable: True， item to enable this function.

3. When video input is required for vehicle retrograde recognition function, the starting command is as follows:

#For single video
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                   -o VEHICLE_RETROGRADE.enable=true \
                                   --video_file=test_video.mp4 \
                                   --device=gpu

#For folder contains one or multiple videos
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                   -o VEHICLE_RETROGRADE.enable=true \
                                   --video_dir=test_video.mp4\
                                   --device=gpu

1. There are two ways to modify the model path:

   • 1.Set paths of each model in ./deploy/pipeline/config/infer_cfg_ppvehicle.yml,For Lane line segmentation, the path should be modified under the LANE_SEG
   • 2.Directly add -o in command line to override the default model path in the configuration file:

python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                   --video_file=test_video.mp4 \
                                   --device=gpu \
                                   -o LANE_SEG.model_dir=output_inference/
                                   VEHICLE_RETROGRADE.enable=true

The result is shown as follow:

Note:

• Automatic judgment condition of lane line middle line: there are two vehicles in opposite directions in the sampled video segment, and the judgment is fixed after one time and will not be updated;
• Due to camera angle and 2d visual angle problems, the judgment of lane line middle line is inaccurate. You can manually enter the middle line coordinates in the configuration file

Features to the Solution

1.In the sampling video segment, judge whether the vehicle is retrograde according to the location of the lane centerline and the vehicle track, and determine the flow chart:

2.Lane line recognition model uses [PaddleSeg]（ https://github.com/PaddlePaddle/PaddleSeg ）Super lightweight segmentation scheme.Train lableit is divided into four categories: 0 Background 1 Double yellow line 2 Solid line 3 Dashed line Lane line recognition filtering Dashed lines;

3.Lane lines are obtained by clustering segmentation results, and the horizontal lane lines are filtered by default. If not, you can modify the filter_flag in lane line seg config file;

4.The vehicles in the horizontal direction are filtered by default when judging the vehicles in the reverse direction. If not, you can modify the filter_horizontal_flag in config file;

5.The vehicle will be judged according to the right driving rule by default.If not, you can modify the keep_right_flag in config file;

Performance optimization measures： 1.Due to the camera's viewing angle, it can be decided whether to filter the lane lines and vehicles in the horizontal direction according to the actual situation;

2.The lane middle line can be manually entered;