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# Pylot - Perception | ||
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**Authors:** Maximilian Jannack | ||
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**Date:** 2.11.2023 | ||
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## [Detection](https://pylot.readthedocs.io/en/latest/perception.detection.html) | ||
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### Obstacle detection | ||
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Pylot provides two options for obstacle detection: | ||
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1. Obstacle detection operator that can use any model that adheres to the Tensorflow `object detection model zoo` | ||
- By default, three models that were trained on 1080p CARLA images (`faster-rcnn`, `ssd-mobilenet-fpn-640`, and `ssdlit-mobilenet-v2`) are provided | ||
- Models that have been trained on other data sets can be easily plugged in | ||
2. Operator that can infer any of the EfficientDet models (not trained on CARLA data, but on the COCO dataset) | ||
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### Traffic light detection | ||
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Uses `Faster RCNN weight` (trained on 1080p CARLA images) | ||
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### Lane detection | ||
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Uses the `Lanenet` model ([repo](https://github.com/MaybeShewill-CV/lanenet-lane-detection)) or canny edge detector. | ||
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--- | ||
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## [Obstacle Tracking](https://pylot.readthedocs.io/en/latest/perception.tracking.html) | ||
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For tracking obstacles across frames. | ||
Uses the `DaSiamRPN` model ([repo](https://github.com/foolwood/DaSiamRPN)) to serially track multiple obstacles. | ||
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## [Depth Estimation](https://pylot.readthedocs.io/en/latest/perception.depth_estimation.html) | ||
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Uses stereo cameras to estimate depth with the `AnyNet` model ([repo](https://github.com/mileyan/AnyNet)). | ||
Configurable camera distance between left and right. | ||
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--- | ||
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## [Segmentation](https://pylot.readthedocs.io/en/latest/perception.segmentation.html) | ||
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Different approaches, such as using the `DRN` model ([repo](https://github.com/ICGog/drn)) for segmenting camera images. | ||
No model with training on CARLA data available, output of segmentation component not used in Pylot right now. | ||
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## [Lidar](https://github.com/erdos-project/pylot/blob/master/pylot/perception/point_cloud.py) | ||
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Pylot contains a few helpful function for handling point clouds from the Lidar sensor. | ||
It can for example merge two point clouds or map the points to a camera image. |