This repo is deprecated. For a newer and improved model, implemented in PyTorch, please refer to this repo.
Module for detecting traffic lights in the CARLA autonomous driving simulator (version: 0.8.4).
Built upon and inspired by https://github.com/experiencor/keras-yolo2.
Instructions and more traffic light detection examples can be found below.
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This module is used along several other modules to implement our version of imitation learning in the CARLA simulator. Results of the core module can be found on this repository
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Model for objection detection is based on tiny yolov2
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Training started with yolov2 coco pretrained weights
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It was first trained on the LISA traffic light detection dataset (~5800 images), and after that on the dataset collected from the CARLA simulator by myself (~1800 images).
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Dataset collected by myself in the CARLA simulator can be found here, annotations can be found here.
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Important note - several images in the dataset are left out of annotations because bounding boxes are too small (too far away). I also filtered (left out) all images that have xmax < 15 when loading the dataset. There is around 70-80 out of ~1800 images that are left out, so it isn't that problematic.
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Pretrained model can be found here.
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To train:
- In the config file set training -> enabled to true
- Put your annotations file in the dataset folder
- In the config file set training -> annot_file_name to the name of your annotations file
- Put your images in the dataset/images folder
- If necessary, adjust parameters in config according to your problem/dataset
- run main.py with -c config.json
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To evaluate:
- In the config file set training -> enabled to false
- Put your annotations file in the evaluation folder
- In the config file set training -> annot_file_name to to the name of your annotations file containing images for evaluation
- Put your images in the evaluation/images folder
- If necessary, adjust parameters in config according to your problem/dataset
- run main.py with -c config.json
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To generate anchors:
- run generate_anchors.py with -c config.json
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Soon to be added:
- Real time traffic light detecting gifs
- Several examples of predictions, more can be found in the out folder
