| Autor: |
Iwin Thanakumar Joseph, S., Shanthini Pandiaraj, N., Sarveshwaran, Velliangiri, Mythily, M. |
| Zdroj: |
Cybernetics & Systems; 2024, Vol. 55 Issue 7, p1441-1468, 28p |
| Abstrakt: |
The recognition and categorization of ships is very significant for maritime security and defense board. The existing SAR image-oriented ship recognition techniques are not capable of real-time applications since the count of SAR sensors is sparse and the contrast of images is minimum. To cope up with certain issues, this article designs an efficient technique for ship detection and classification by introducing hybrid optimization-based deep learning technique. Here, segmentation is done using bounding box segmentation. The ships on the ocean surface are effectively detected using deep convolutional neural network, where the network is trained utilizing devised exponential mayfly optimization algorithm (EMO). The newly designed EMO is derived by the combination of the exponential weighted moving average (EWMA) and mayfly optimization algorithm (MA). The type of ships is classified effectively utilizing deep residual neural network (DRN) and the network is trained by introducing the proposed political exponential mayfly optimization algorithm (PoEMO). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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