首页|Ship target detection method based on improved YOLOv8 for SAR images
Ship target detection method based on improved YOLOv8 for SAR images
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NETL
NSTL
Taylor & Francis
ABSTRACT Due to the complex background and less effective information in low-resolution and noisy images, the detection of small ships in SAR images suffers from low detection rate and high false alarm rate. In order to solve the above problems, this paper proposes a method to detect the small ship targets in SAR images based on improved YOLOv8. Firstly, the deformable convolutional networks are added to the leading network to improve feature extraction. Then, the efficient multi-scale attention module is fused with the backbone network to enhance the detection effect of small targets. The weighted intersection over union loss function is used to optimize the regression process of the prediction frame and the detection frame to enhance the localization capacity of small targets. Finally, there is an addition of a specialized small target detection layer, and a reconstruction of the feature extraction and fusion network to enhance the detection performance of small ship targets in SAR images. To verify the effectiveness and robustness of the method, we conduct experiments on SSDD and HRSID. The proposed method achieves high detection accuracy while also offering a more compact model size and less computation time compared to other prevalent methods.
YOLOv8network reconstructiondeformable convolutional networksefficient multi-scale attentionweighted intersection over union loss functionsynthetic aperture radar target detection
NETL
NSTL
Taylor & Francis
