Semantic Segmentation of Marine Radar Images using Convolutional Neural Networks
| Autor: | Jinwhan Kim, Keunhwan Kim |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ComputerApplications_COMPUTERSINOTHERSYSTEMS Image segmentation Convolutional neural network law.invention Radar engineering details law Computer Science::Computer Vision and Pattern Recognition Radar imaging Segmentation Computer vision Noise (video) Artificial intelligence Radar business Physics::Atmospheric and Oceanic Physics |
| Zdroj: | OCEANS 2019 - Marseille. |
| DOI: | 10.1109/oceanse.2019.8867504 |
| Popis: | Marine radar is essential for ship navigation and control. Several studies exist that are focused on eliminating unwanted noise signals to properly detect targets in radar images. However, in general, radar image processing techniques are sensitive to radar sensor specifications and operating conditions, and ad-hoc image preprocessing steps are required to adjust many parameters to achieve an overall satisfactory performance. This paper addresses the semantic segmentation of marine radar images using a convolutional neural network (CNN). SegNet is utilized as the CNN algorithm for the semantic segmentation. In order to improve the performance of the CNN considering the measurement characteristics of the radar, Cartesian coordinate system based images are transformed into polar coordinate systems. The Cartesian and polar images are used and the original SegNet model is modified to train the network in a variety of methods. The results show the feasibility of using a deep learning network to achieve effective and robust semantic segmentation in several different sets of marine radar images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|