Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/matlab-deep-learning/pretrained-spatial-CNN
Spatial-CNN for lane detection in MATLAB.
https://github.com/matlab-deep-learning/pretrained-spatial-CNN
autonomous-driving computer-vision deep-learning deeplearning detected-lanes lane lane-detection lane-detector lane-lines-detection matlab pretrained-models scnn scnn-network spatial-cnn
Last synced: 3 months ago
JSON representation
Spatial-CNN for lane detection in MATLAB.
- Host: GitHub
- URL: https://github.com/matlab-deep-learning/pretrained-spatial-CNN
- Owner: matlab-deep-learning
- License: other
- Created: 2021-05-06T06:13:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-09-23T15:03:21.000Z (about 3 years ago)
- Last Synced: 2024-07-27T12:47:06.212Z (4 months ago)
- Topics: autonomous-driving, computer-vision, deep-learning, deeplearning, detected-lanes, lane, lane-detection, lane-detector, lane-lines-detection, matlab, pretrained-models, scnn, scnn-network, spatial-cnn
- Language: MATLAB
- Homepage:
- Size: 32.5 MB
- Stars: 24
- Watchers: 5
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
- MATLAB-Deep-Learning-Model-Hub - Spatial-CNN - deep-learning/pretrained-spatial-CNN)|<img src="Images/lanedetection.jpg" width=150>| (Object Detection <a name="ObjectDetection"/> / Robotics)
README
# Lane Detection Using Deep Learning
This repository implements a pretrained Spatial-CNN (SCNN)[1] lane detection model in MATLAB®.
## Requirements
- MATLAB® R2021a or later.
- Deep Learning Toolbox™.
- Computer Vision Toolbox™.
- Automated Driving Toolbox™.
## Overview
This repository implements SCNN with VGG-16 as the backbone. The pretrained network is trained to detect lanes in the image. The network is trained using [CULane](https://xingangpan.github.io/projects/CULane.html) dataset[1].
Spatial-CNN (SCNN) uses slice-by-slice convolutions on the feature maps obtained by layer-by-layer convolutions since the spatial information can be reinforced via inter-layer propagation. This helps in detecting objects with strong structure prior but less appearance clues such as lanes, poles, or trucks with occlusions.
## Getting Started
Download or clone this repository to your machine and open it in MATLAB®.
### Setup
Add path to the source directory.
```
addpath('src');
```
### Download and Load the Pretrained Network
Use the below helper to download and load the pretrained network. The network will be downloaded and saved in `model` directory.
```
model = helper.downloadSCNNLaneDetection;
net = model.net;
```
### Detect Lanes Using SCNN
This snippet includes all the steps required to run SCNN model on a single RGB image in MATLAB®. Use the script `spatialCNNLaneDetectionExample.m` to run the inference on single image.
```
% Specify Detection Parameters.
params = helper.createSCNNDetectionParameters;
% Specify the executionEnvironment as either "cpu", "gpu", or "auto".
executionEnvironment = "auto";
% Read the test image.
path = fullfile("images","testImage.jpg");
image = imread(path);
% Use detectLaneMarkings function to detect the lane markings.
laneMarkings = detectLaneMarkings(net, image, params, executionEnvironment);
% Visualize the detected lanes.
fig = figure;
helper.plotLanes(fig, image, laneMarkings);
```
Alternatively, you can also run the SCNN model on sample videos. Use the script `spatialCNNLaneDetectionVideoExample.m` to run the inference on a driving scene.
### Result
Left-side image is the input and right-side image shows the detected lanes. The image is taken from the [Panda Set](https://scale.com/open-datasets/pandaset) dataset[2].
Sample video output generated by the script `spatialCNNLaneDetectionVideoExample.m`.
## Evaluation Metrics
The model is evaluated using the method specified in [1].
| Dataset | Error Metric | IOU | Result |
| ------------- | ------------- | ------------- | ------------- |
| CULane | F-measure | 0.3 | 73.45 |
| CULane | F-measure | 0.5 | 43.41 |
## Spatial-CNN Algorithm Details
The SCNN network architecture is illustrated in the following diagram.
|
|
|:--:|
|**Fig.1**|
The network takes RGB images as input and outputs a probability map and confidence score for each lane. The pre-trained SCNN model trained on [CULane](https://xingangpan.github.io/projects/CULane.html) can detect maximum of 4 lanes( 2 driving lanes and 2 lanes on either side of the driving lane). The probability map predicted by the network has 5 channels (4 lanes + 1 background). Lanes with confidence score less than 0.5 are ignored. To generate the detections the probability map is processed and curves are fit.
SCNN network in this repository has 4 message passing layers in sequence in the directions top-to-bottom, bottom-to-top, left-to-right, and right-to-left with kernel size of 9 and are represented by up-down, down-up, left-right, and right-left respectively in Fig.1. The message passing layers are special layers that apply slice-by-slice convolutions within the feature map[1]. These layers are implemented as a custom nested deep learning layer. For more information about the custom nested deep learning layer, see [Define Nested Deep Learning Layer](https://www.mathworks.com/help/deeplearning/ug/define-nested-deep-learning-layer.html).
## References
[1] Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang. "Spatial As Deep: Spatial CNN for Traffic Scene Understanding" AAAI Conference on Artificial Intelligence (AAAI) - 2018
[2] [Panda Set](https://scale.com/open-datasets/pandaset) is provided by Hesai and Scale under the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0)
## See also
[Visual Perception Using Monocular Camera](https://www.mathworks.com/help/driving/ug/visual-perception-using-monocular-camera.html)
Copyright 2021 The MathWorks, Inc.