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Found 3 papers, 3 papers with code
Unsupervised semantic segmentation of high-resolution UAV imagery for road scene parsing
In this paper, an unsupervised road parsing framework that leverages recent advances in vision language models and fundamental computer vision model is introduced. Initially, a vision language model is employed to efficiently process ultra-large resolution UAV images to quickly detect road regions of interest in the images.
Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.
An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model
Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images.
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