Road Detection Program Using CNN Methodology

Application Context

In modern transportation systems, road identification and extraction constitutes a critical research domain. Accurate road detection enables traffic management departments to better understand road network conditions, providing valuable data support for traffic planning and flow prediction. Consequently, developing efficient and precise road detection methodologies holds significant importance.

Key Technology

Convolutional Neural Networks (CNNs) have been widely adopted in road detection research. CNN architectures excel at learning hierarchical features from image data for classification and recognition tasks, demonstrating superior performance in road identification. Through CNN model training and inference on road imagery, the system achieves accurate road detection and extraction in both urban and rural environments. This approach employs convolutional layers for feature extraction, pooling layers for spatial hierarchy, and fully connected layers for classification. The methodology effectively handles non-remote sensing images, showcasing strong generalization capabilities and adaptability across various road types and environmental conditions.

This brief introduction to road detection aims to provide valuable references and insights for researchers and practitioners in related fields.