Abstract
Panoramic images are widely used in many scenes, especially in virtual reality and street view capture. However, they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images. This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks (FCN). FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction. In this study, we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data. The results show that in both pre-trained model and fine-tuning, transformed images have better prediction results than panoramic images.
Original language | English |
---|---|
Title of host publication | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Subtitle of host publication | ISPRS Geospatial Week 2019 |
Publisher | International Society for Photogrammetry and Remote Sensing (ISPRS) |
Pages | 13-20 |
Number of pages | 8 |
Volume | 42 |
Edition | 2/W13 |
DOIs | |
Publication status | Published - 4 Jun 2019 |
Event | 4th ISPRS Geospatial Week 2019 - University of Twente, Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 Conference number: 4 https://www.gsw2019.org/ |
Publication series
Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
---|---|
Publisher | Copernicus |
ISSN (Print) | 2194-9034 |
Conference
Conference | 4th ISPRS Geospatial Week 2019 |
---|---|
Country/Territory | Netherlands |
City | Enschede |
Period | 10/06/19 → 14/06/19 |
Internet address |
Keywords
- ITC-GOLD