- Detect traffict lights from traffic iamges (videos)
- Classify the lights into 3 colors
- This is used for self driving car
- Use transfer learning of pre-trained model
- Atchitecture
- Use pretrained Mobile Net (CNN) for quick and memory saving detection, since speed is reauired. Mobile Net is trained for modification for this task
- Use SSD (Single Shot Detection) to detect the location and the classification at the same time
- SSD_Mobilenet 11.6.17 version from Tensorflow model zoo
- Training Data: Used Bosch Small Traffic Light dataset (already annoted the class and positions) https://github.com/bosch-ros-pkg/bstld
- See video above
- Detected red lights well, but still some missing lights
- Detecting other color lights not tested
-
Download Bosch dataset and convert that into tfrecords format
-
Setup the transfer learning (Following comes from comes from repo of Alex Lechner's repo . Appreciate the describing the complex Tensorflow (of an older version of TF that has limited info.) setup!)
-
Install Tensorflor 1.4:
pip install tensorflow==1.4(newer version is not compatible!)- (for Linux machine with GPU,
pip install tensorflow-gpu==1.4)
- (for Linux machine with GPU,
-
Install relevant packages:
pip install pillow lxml matplotlib -
Download
protoc-3.4.0-win32.zipto extract toC:\Program Files\protoc-3.4.0-win32- (for Linux:
sudo apt-get install protobuf-compiler python-pil python-lxml python-tk)
- (for Linux:
-
Create
/TensorFlowdirectory -
Clone Models repository into
TensorFlowdirectory:git clone https://github.com/tensorflow/models.git -
Navigate to the
/modelsdirectory and change the repo version to:git checkout f7e99c0 -
Navigate to
researchfolder, and creating .py files by executing:"C:\Program Files\protoc-3.4.0-win32\bin\protoc.exe" object_detection/protos/*.proto --python_out=. -
Add following paths to environment variable
PYTHONPATH(you may need to restart the terminal to activate the env variables):modelsmodels/researchmodels/research/slimmodels/research/object_detection
-
run:
python builders/model_builder_test.pyat/object_detectiondirectory -
configure a tensorflow config file and place in
/configfolder. -
train the model:
python train.py --logtostderr --train_dif=./models/train --pipeline_config_path=./config/<TFMODELCONFIG.config> -
freeze the model:
python export_inference_graph.py --input_type image_tensor --pipeline_config_path./config/ssd_mobilenet_c1-B.config --trained_checkpoint_prefix ./models/train/model.ckpt-2000 --output_directory models
- Tensorflow Object Detection install https://github.com/tensorflow/models.git
- Bosch Small Traffic Light dataset
- This repository is not fully updated, there maybe some parts that are not updated
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent