Adaptability Study of Multi-Object Tracking Algorithms in Maritime Radar Scenarios

Addressing the challenges posed by low Signal-toNoise Ratio, weak texture, and strong clutter interference in maritime radar imagery, this paper proposes a performance evaluation framework for object detection and Multi-Object Tracking based on YOLOv8. The study aims to systematically verify the applicability of the "Tracking-by-Detection" paradigm in maritime radar scenarios and to deeply investigate the practical value of appearance features in data association. Leveraging a self-constructed dataset of 1,816 high-precision annotated radar images, this paper first optimized and trained a YOLOv8 detector tailored to radar characteristics, establishing a high-performance detection baseline with a mAP50 of 99.42%. On this basis, a comparative evaluation was conducted on over ten mainstream tracking algorithms, including SORT, ByteTrack, and SparseTrack. In particular, addressing the lack of visual details in radar targets, this paper constructed a radar-specific Reidentification dataset and trained a multi-scale OSNet model for ablation studies.