Ship Target Detection in SAR Images Based on Multi-Scale Dynamic Fusion Model of YOLOv8
DOI:
https://doi.org/10.54097/r978ba16Keywords:
SAR, deep learning, YOLO, Multi-scale dynamic fusion, Ship target detectionAbstract
Target detection is an important part of radar image interpretation. The use of SAR technology combined with deep learning network models has become an important means of ship target detection. Compared with traditional methods, deep learning has powerful data processing and feature extraction capabilities. Therefore, this paper proposes a multi-scale dynamic fusion (DFS) detection model for target detection based on YOLOv8. The model includes three parts: multi-scale feature extraction and fusion, dynamic alignment, and attention mechanism feature enhancement. Finally, experiments were conducted on the SAR-ship dataset. The experimental results show that the detection results of the model reach 96.4%.
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