12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block

      Preprint
      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          For the self-driving and automatic parking, perception is the basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. In this paper, we use the semantic segmentation method to segment the area and classify the class of lane makings and parking slots on panoramic surround view (PSV) dataset. We propose the DFNet and make two main contributions, one is dynamic loss weights, and the other is residual fusion block (RFB). Dynamic loss weights are varying from classes, calculated according to the pixel number of each class in a batch. RFB is composed of two convolutional layers, one pooling layer, and a fusion layer to combine the feature maps by pixel multiplication. We evaluate our method on PSV dataset, and achieve an advanced result.

          Related collections

          Most cited references10

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Pyramid Scene Parsing Network

            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation

              Bookmark
              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

                Bookmark

                Author and article information

                Journal
                11 June 2018
                Article
                1806.07226
                32a6058a-9c9d-41f7-b0fd-cce7816480fb

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                6 pages,3 figures
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

                Comments

                Comment on this article