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Abstract
Synthetic Aperture Radar (SAR) is renowned for its all-weather capabilities, exceptional penetration, and high-resolution imaging, making SAR-based ship detection crucial for maritime surveillance and sea rescue operations. However, various challenges, such as blurred ship contours, complex backgrounds, and uneven scale distribution, can impede detection performance improvement. In this study, we propose FastPFM, a novel ship detection model developed to address these challenges. Firstly, we utilize FasterNet as the backbone network to reduce computational redundancy, enhancing feature extraction efficiency and overall computational performance. Additionally, we employ the Feature Bi-level Routing Transformation model (FBM) to obtain global feature information and enhance focus on target regions. Secondly, the PFM module is engineered to collect multi-scale target information effectively by establishing connections across stages, thereby improving fusion of target features. Thirdly, an extra target feature fusion layer is introduced to enhance small ship detection precision and accommodate multi-scale targets. Finally, comprehensive tests on SSDD and HRSID datasets validate FastPFM's efficacy. Compared to the baseline model YOLOX, FastPFM achieves a 5.5% and 4.4% improvement in detection accuracy, respectively. Furthermore, FastPFM demonstrates comparable or superior performance to other detection algorithms, achieving 92.1% and 83.1% accuracy on AP50, respectively.
Disclosure statement
No potential conflict of interest was reported by the author(s).