At Camegie Mellon University, we have two new vision systems for outdoor road following. The first system, called SCARF (Supervised Classification Applied to Road Following), is designed to be fast and robust when the vehicle is running in both sunshine and shadows under constant illumination. The second system, UNSCARF (UNSupervised Classification Applied to Road Following), is slower, but pro...

In this paper, we present a task-specific segmentation method that incorporates semantic knowledge into datadriven segmentation process through different region merge scores. Starting from a simple region growing algorithm which results in over-segmented regions, we apply region merging method designed specifically for each task such as road extraction or vegetation area identification. Further...

This paper introduces a real-time workflow for implementing neural networks in the context of autonomous driving. The UNet architecture is specifically selected road segmentation due to its strong performance and low complexity. To further improve model's capabilities, Local Binary Convolution (LBC) incorporated into skip connections, enhancing feature extraction, elevating Intersection over Un...

A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields vision, are widely used autonomous driving. segmentation for uses deep learning methods various large sample datasets to train a proper model. Due the importance this task, accurate robust models should be trained perform properly differen...

Road extraction in remote sensing images is of great importance for a wide range applications. Because the complex background, and high density, most existing methods fail to accurately extract road network that appears correct complete. Moreover, they suffer from either insufficient training data or costs manual annotation. To address these problems, we introduce new model apply structured dom...

Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based methods have had limited receptivity failed effectively capture long-distance features. On the other hand, transformer-b...