Multifeature Transformation and Fusion-Based Ship Detection With Small Targets and Complex Backgrounds

With the development of deep learning, synthetic aperture radar (SAR) image ship detection based on the convolutional neural network has made significant progress. However, there are two problems: 1) the false alarm detection rate is high due to complex background and coherent speckle noise interference and 2) for smaller ship targets, missed detection is prone to occur. In this letter, a novel ship detection model based on multifeature transformation and fusion (MFTF-Net) is proposed to address the issues. First, to avoid the randomness of initial point selection and the influence of outlier points, the anchor frame clustering approach based on the K -medians++ algorithm is presented to cluster the object candidate frames. Second, the low-level feature information is passed to the high level by constructing a local enhancement network; then, an improved transformer structure is introduced to replace the last convolutional block of the backbone network to obtain rich contextual information. Finally, a four-scale residual feature fusion network is designed, which fully fuses the object's detailed and semantic information. In addition, improved convolutional block attention module (CBAM) and squeeze and excitation (SE) attention mechanisms are applied in the lower two layers and upper two layers of the network output to reduce the interference of confusing information, respectively. The experimental results demonstrate that the proposed method is superior to the state-of-the-art 13 baseline models on SAR ship detection dataset (SSDD), high-resolution SAR images dataset (HRSID), and SAR-ship-dataset public datasets in terms of the mean average precision (mAP), recall, accuracy, and F1 metrics.