Understanding ADAS Road Lane Detection

Deep learning-based lane detection system using convolutional neural networks (CNN) to detect lanes in road videos. The model processes video input, performs lane detection, and outputs a video with detected lane markings overlaid.

Overview

The project is divided into three main components:

1. Model: A neural network (LaneNet) designed for lane detection, built with PyTorch.
2. Training: The training script (train.py) which trains the model on lane detection data.
3. Detection: The detection script (detect_lanes.py) that uses the trained model to detect lanes in video footage.
4. Utility: Helper functions and datasets for training and detection.

Demo

timelapse	short-demo

Requirements

• Python 3.x
• PyTorch
• OpenCV
• MoviePy
• Scikit-learn
• Numpy

How the Model Works

The lane detection model is based on a deep learning architecture designed to identify lane markings from road video frames. The model utilizes a convolutional neural network (CNN), which learns to detect lane features from the training data.

Input:

The model takes video frames as input, each of which is processed to detect lane markings. The frames are typically of a road environment with varying lighting, road types, and lane structures.

Architecture:

The neural network (LaneNet) is a fully convolutional network (FCN) designed to detect lane boundaries from road images. The architecture includes the following components:

• Convolutional Layers: These layers extract spatial features from the input images.
• Pooling Layers: These reduce the dimensionality of the image and help in detecting key lane features at multiple scales.
• Fully Connected Layers: These layers predict the final output for lane classification.
• Output Layer: The output layer produces the lane segmentation mask for each frame, which indicates where lanes are present.

Training:

The model is trained using images and their corresponding lane segmentation labels. The CNN learns to identify lane markings from the pixel-level ground truth provided in the training dataset.

Loss Function:

The training process utilizes a cross-entropy loss function, which measures the difference between the predicted lane segmentation mask and the actual ground truth. The optimizer adjusts the weights of the network to minimize this loss during training.

Post-Processing:

After obtaining the lane mask for each frame, post-processing is applied to refine the detected lanes. This may involve filtering out noise, smoothing the lane boundaries, and fitting lane lines to the detected lane pixels.

Training Process

Training Data:

For training the model, you need a dataset of road images along with corresponding lane markings. The dataset should contain images from real-world road scenarios with annotated lane markings.

Steps for Training:

1. Prepare Data: Gather images of roads with lane markings. For each image, create a corresponding label that indicates the lane positions.
2. Train Model: Use the training script (train.py) to train the model using these images and labels. The script will load the dataset, process the images, and start the training process using the CNN.
3. Monitor Training: During training, the model's performance is evaluated using metrics like accuracy or intersection over union (IoU) between the predicted lane mask and ground truth.
4. Model Saving: After training, the model weights are saved in a file called model.pth, which can be used for inference or testing.