Superpixel clustering with deep features for unsupervised road segmentation

by   Shunta Saito, et al.
Indiana University Bloomington
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Vision-based autonomous driving requires classifying each pixel as corresponding to road or not, which can be addressed using semantic segmentation. Semantic segmentation works well when used with a fully supervised model, but in practice, the required work of creating pixel-wise annotations is very expensive. Although weakly supervised segmentation addresses this issue, most methods are not designed for road segmentation. In this paper, we propose a novel approach to road segmentation that eliminates manual annotation and effectively makes use of road-specific cues. Our method has better performance than other weakly supervised methods and achieves 98 the performance of a fully supervised method, showing the feasibility of road segmentation for autonomous driving without tedious and costly manual annotation.


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