Abstract
Obstacle detection and recognition are core problems in the field of autonomous driving. For urban road surfaces, this paper proposes a complete obstacle recognition method. Firstly, this paper differs from semantic segmentation and target detection frameworks but represents the region of interest based on stixel and improves the traditional scheme of generating stixel. Then this paper classifies the different obstacles in the region of interest based on the improved deep learning network DenseNet. Finally, recognition of individual obstacles is performed. The experimental results show that the method in this paper can effectively represent the region of interest consisting of the first layer of obstacles and achieve obstacle recognition more accurately.
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