Robust and Automatic Skyline Detection Algorithm Based on MSSDN
Fan Guo*, Yuxiang Mai**, Jin Tang*,, Yu Huang*, and Lijun Zhu*
*School of Automation, Central South University
Changsha 410083, China
**School of Computer Science and Engineering, Central South University
Changsha 410083, China
Automatic detection of the skyline plays an important role in several applications, such as visual geo-localization, flight control, port security, and mountain peak recognition. Existing skyline detection methods are mostly used under common weather conditions; however, they do not consider bad weather situations, such as rain, which limits their application in real scenes. In this paper, we propose a multi-stream-stage DenseNet to detect skyline automatically under different weather conditions. This model fully considers the adverse factors influencing the skyline and outputs a probability graph of the skyline. Finally, a dynamic programming algorithm is implemented to detect the skyline in images accurately. A comparison with the existing state-of-the-art methods proves that the proposed model shows a good performance under rainy or common weather conditions and exhibits the best detection precision for the public database.
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