Lightweight SAR ship detection algorithm based on attention mechanism

In recent years, with the rapid development of deep learning, the application in the field of synthetic aperture radar (SAR) image ship detection has received increasing attention. Most of the existing algorithms suffer from the problems of more network parameters and larger computation, so they are usually difficult to be deployed on embedded devices with limited storage and computational resources. The lightweight detection network cannot achieve good detection accuracy in complex scenes due to its small number of parameters and insufficient feature extraction and fusion for small-sized ships. Aiming at the above problems, in this paper, based on the YOLOv5 detection algorithm, the backbone and neck networks are designed to be lightweight, and the improved spatial feature pyramid module SimSPPFC is proposed to replace the SPPF module in YOLOv5, and the convolutional block attention module (CBAM) is used to construct an efficient feature extraction module to improve the feature extraction and selection capability of the network, and the weighted average introduced to the weighted non-maximum suppression (NMS) is introduced to replace the traditional NMS as the post-processing algorithm to further improve the localization accuracy of the prediction box. Experiments show that the proposed algorithm achieves 97.7% AP₅₀ and 66.8% AP_50:95 on the SSDD dataset with 0.438 M number of parameters and 1.1GFLOPs computational complexity. The algorithm takes into account both the detection accuracy and efficiency, and realizes a good trade-off between algorithmic lightweighting and detection accuracy.