Road Detection

Road detection, the automated identification of roads in images or point clouds, is crucial for autonomous driving, mapping, and remote sensing applications. Current research emphasizes improving accuracy and efficiency through multi-modal data fusion (combining LiDAR, camera, and satellite imagery), advanced deep learning architectures like U-Net and its variants (e.g., ResUnet, D-LinkNet), and innovative techniques such as knowledge distillation and self-supervised learning to address data scarcity. These advancements enable more robust and reliable road detection in diverse and challenging environments, impacting fields ranging from autonomous navigation to infrastructure management.