Self-attention mechanism-based SAR for YOLO-v3 maritime ships image target detection.
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence(2022)
Abstract
In recent years, China's maritime construction has been gradually strengthened, and the security of our territorial waters has become a top priority. In this paper, we propose a self-attentive mechanism-based target detection model for YOLO-v3SAR images, and through experiments, we add a self-attentive mechanism before and after the feature fusion part for target detection, and compare the accuracy, we conclude that adding a self-attentive mechanism before each predicted feature layer can effectively improve the detection accuracy. After adding the self-attention mechanism, the detection accuracy of SSDD dataset increases by 10%, Increased from 84.7 to 94.3%, and that of Ship-dataset dataset increases by 9%, from 79% to 88%. The experiments prove that the improved algorithm model is adapted to SAR image target detection and reaches the advanced level, which provides a new idea for SAR image target detection of maritime ships.
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