Lane Detection Dataset Morelos Sergio

The Morelos Road Dataset is designed for lane detection and computer vision research, offering a diverse collection of 22,566 images extracted from 41 video clips captured on roads across Morelos, Mexico. It provides a comprehensive benchmark for autonomous driving, road safety analysis, and AI-powered navigation systems.

Objective

This dataset aims to support the development of robust lane detection algorithms, particularly in challenging environments where traditional systems struggle. It serves as a critical resource for training and evaluating deep learning models in real-world driving conditions.

Key Features

• Diverse Road Scenarios: Includes sharp curves, narrow roads, and complex lane structures.
• Challenging Weather Conditions: Captures rain, fog, and adverse lighting for realistic model training.
• Low-Light & Shadow Variations: Supports nighttime vision system optimization.
• High-Quality Annotations: Designed for deep learning applications in lane detection.

Applications

• Autonomous Driving Research – Enhancing self-driving vehicle perception systems.
• Lane Detection & Road Safety Analysis – Improving AI-based traffic management.
• Computer Vision & Deep Learning – Benchmark dataset for model evaluation.

This dataset is sourced from Kaggle.