A Real-Time SAR Ship Detection Method Based on Improved CenterNet for Navigational Intent Prediction

Abstract

Utilizing massive spatio-temporal sequence data and real-time synthetic aperture radar (SAR) ship target monitoring technology, it is possible to effectively predict the future trajectories and intents of ships. While real-time monitoring technology validates and adjusts spatio-temporal sequence prediction models, it still faces challenges, such as manual anchor box sizing and slow inference speeds due to large computational parameters. To address this challenge, a SAR ship target real-time detection method based on CenterNet is introduced in this article. The proposed method comprises the following steps. First, to improve the feature extraction capability of the original CenterNet network, we introduce a feature pyramid fusion structure and replace upsampled deconvolution with Deformable Convolution Networks (DCNets), which enable richer feature map outputs. Then, to identify nearshore and small target ships better, BiFormer attention mechanism and spatial pyramid pooling module are incorporated to enlarge the receptive field of network. Finally, to improve accuracy and convergence speed, we optimize the Focal loss of the heatmap and utilize Smooth L1 loss for width, height, and center point offsets, which enhance detection accuracy and generalization. Performance evaluations on two SAR image ship datasets, HRSID and SSDD, validate the method's effectiveness, achieving Average Precision (AP) values of 82.87% and 94.25%, representing improvements of 5.26% and 4.04% in AP compared to the original models, with detection speeds of 49 FPS on both datasets. These results underscore the superiority of the improved CenterNet method over other representative methods for SAR ship detection in overall performance.

Keywords

• CenterNet
• deep learning
• real-time monitoring
• spatio-temporal sequence prediction