Early Days (Pre-Deep Learning):
1980s-1990s: Focus on traditional methods like: Edge Detection: Using filters to identify sharp changes in intensity suggesting lane lines. Color Segmentation: Isolating pixels within a specific color range (yellow/white) potentially belonging to lane lines. Model-Based Approaches: Defining a mathematical model for lane lines and searching for lines that fit the model in the image. Limitations: Sensitive to lighting variations, shadows, and complex lane markings. Deep Learning Era (2010s-Present):
Revolution: Deep learning, particularly Convolutional Neural Networks (CNNs), emerged as the dominant approach. CNN Advantages: Learn complex features directly from image data, handling variations in lighting and lane markings. More robust and adaptable to real-world driving scenarios. Deep Learning Techniques: Segmentation-based: Classify each pixel as belonging to a lane line, road surface, or background. (e.g., LaneNet, SCNN) Anchor-based: Similar to object detection, using predefined anchors to predict lane line deviations. Current Trend: 3D Lane Detection: Moving beyond 2D image analysis to incorporate depth information for a more accurate understanding of the road environment.
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