Project Title: Road Lane Line Detection

Objective:

The goal of this project is to develop a computer vision model that can detect road lane lines in images or video streams from a vehicle’s camera. This is an essential component of autonomous driving systems, as lane detection helps the vehicle maintain its position on the road and ensures safe navigation by following road boundaries.

Key Components:

Data Collection:

Use publicly available datasets that include images or videos of road scenes with clearly marked lane lines. Popular datasets include:

Tusimple Lane Detection Dataset: Contains images of road scenes with labeled lane markings for autonomous driving research.

CULane Dataset: A dataset for lane detection in challenging real-world driving conditions, including nighttime and rainy conditions.

Kitti Dataset: Includes data captured from cameras mounted on vehicles, including labeled lane markings.

Data can also be gathered using simulation tools (e.g., CARLA, LGSVL Simulator) to generate synthetic data for training.

Data Preprocessing:

Image Augmentation: Apply transformations such as rotation, scaling, and flipping to artificially expand the dataset and improve the model’s robustness.

Region of Interest (ROI) Selection: Focus on the part of the image that contains the road by cropping the top portion (e.g., sky) and leaving the bottom portion where lane lines are visible.

Grayscale Conversion: Convert the images to grayscale to simplify processing, as lane line detection primarily relies on contrasts in the road surface.

Edge Detection: Use edge detection algorithms like Canny edge detector to highlight lane markings by detecting abrupt changes in intensity in the image.

Model Selection:

Traditional Computer Vision Approaches:

Hough Transform: A classic technique for detecting straight lines, which can be applied to detect lane markings after performing edge detection.

Sliding Window Technique: A common approach where a window slides across the image to find lane line pixels and build up a robust line model.

Deep Learning Models:

Convolutional Neural Networks (CNNs): Use CNNs for feature extraction from images, detecting lane lines in complex environments with varying road conditions.

Segmentation Models: Use semantic segmentation models like U-Net or FCN (Fully Convolutional Networks) to segment the image and identify lane pixels.

LaneNet: A deep learning model specifically designed for lane detection that uses CNNs to classify pixels into lane markings and background, providing precise lane positions.

LSTMs (Long Short-Term Memory): For video or real-time stream processing, LSTMs can be used to track lane lines over time, making predictions based on previous frames.

Model Training:

Loss Function: Train the model using a pixel-wise classification loss (e.g., cross-entropy loss) for segmentation tasks or a line-fitting loss function for regression tasks.

Data Augmentation: Use various augmentations (e.g., lighting changes, weather conditions) to make the model robust to different road environments.

Batch Normalization: Apply techniques like batch normalization to stabilize training and accelerate convergence.

Split the data into training, validation, and test sets to ensure the model generalizes well to new, unseen data.

Model Evaluation:

Accuracy: Measure the accuracy of the lane detection in terms of pixel-level accuracy (how many pixels in the image are correctly classified as lane markings).

Intersection over Union (IoU): Use IoU to evaluate how well the predicted lane lines overlap with the ground truth lane lines.

End-to-End Lane Detection: Evaluate the model on its ability to detect lane lines across video frames or real-time input from a camera.

Real-time Processing: Evaluate the model's ability to process video frames in real-time, ensuring that it can detect lane lines quickly enough for autonomous driving applications.

Testing and Validation:

Simulation Testing: Test the model in simulated environments where lane detection can be evaluated under various conditions (e.g., sharp curves, different lighting, and weather).

Real-World Testing: Once the model is sufficiently trained, test it on a real vehicle’s camera or a drone-mounted camera in actual driving conditions to validate its robustness.

Deployment:

Deploy the model to an embedded system or onboard computer (e.g., Raspberry Pi, NVIDIA Jetson) that processes images in real-time.

Edge Processing: Ensure that the model runs efficiently on low-latency devices for real-time lane detection, which is crucial for autonomous driving systems.

Integrate the lane detection system with other autonomous vehicle subsystems (e.g., path planning, control systems) to enable the vehicle to adjust its steering based on lane positioning.

Ethical and Safety Considerations:

Ensure the lane detection system is reliable in various real-world driving conditions (e.g., nighttime, fog, heavy rain) to prevent accidents.

The model should be robust enough to handle edge cases like faded lane markings, unusual road signs, or complex intersections.

Adhere to safety standards for autonomous vehicles and ensure the system can fail safely in case of model errors (e.g., alerting the driver or taking corrective action).

Outcome:

A lane detection system capable of detecting and tracking lane markings in real-time video streams from a vehicle’s camera. This system can be integrated into autonomous driving technologies to help vehicles stay in their lanes, navigate roads, and improve overall driving safety.