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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 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 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.

<img src="images/SCNN_architecture.png" width="949" height="385" title="Fig.1" />
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 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.

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 is provided by Hesai and Scale under the CC-BY-4.0 license