SAR ship target detection method based on improved Nanodet
jin dengfeng, Wei Xu, huang pingping, Tan Weixian, Yaolong Qi · 2023
Rapid extraction of ship target information in Synthetic Aperture Radar (SAR) images plays an important role in sea surface monitoring and military prevention. However, the existing detection algorithms have disadvantages such as large model volume and slow detection speed, which are not suitable for the requirements of future star-earth integrated target detection. To solve these problems, this article proposes a SAR ship target detection method based on the improved Nanodet algorithm. To solve the problem of multi-level feature map fusion, the Ghost-pan module is added to the network to enlarge the receptive field and better fuse multi-scale features. At the same time, Resnet18 is used instead of the original backbone network, and depth-wise separable convolution is used instead of ordinary convolution to reduce the model parameter volume and improve detection efficiency. Conducted ablation experiments on the SAR dataset, and the results show that the proposed method achieves better accuracy and faster detection speed.