Fusion of Ship Perceptual Information for Electronic Navigational Chart and Radar Images based on Deep Learning
| Autor: | Chuang Zhang, Muzhuang Guo, Chen Guo, Daheng Zhang, Zongjiang Gao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry Deep learning ComputerApplications_COMPUTERSINOTHERSYSTEMS Ocean Engineering Image processing 02 engineering and technology Oceanography Sensor fusion 020901 industrial engineering & automation Chart GNSS applications Radar imaging 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Electronic navigational chart |
| Zdroj: | Journal of Navigation. 73:192-211 |
| ISSN: | 1469-7785 0373-4633 |
| DOI: | 10.1017/s0373463319000481 |
| Popis: | Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety. |
| Databáze: | OpenAIRE |
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