YOLOShipTracker: Tracking ships in SAR images using lightweight YOLOv8

• A YOLOShipTracker model was developed for ship tracking using the YOLOv8 model. • HGNetv2 Backbone and Lightweight Neck improve SAR feature extraction. • Decoupled head and knowledge distillation reduce parameters and maintain accuracy. • Development of C-BIoU for accurate tracking with high performance and real-time capability. This paper presents a novel approach to tracking ships in Synthetic Aperture Radar (SAR) images based on an improved lightweight YOLOv8 Nano (YOLOv8n), specially devised to improve efficiency without compromising accuracy. In our method, we replaced the heavy backbone and neck of YOLOv8 with HGNetv2 and slim-neck, respectively. We also implemented a lightweight decoupling head using EMSConvP. Additionally, we integrated a knowledge distillation module to further enhance detection capabilities. Furthermore, we conducted extensive experiments on the short-time sequence SAR dataset to demonstrate superior accuracy metrics compared to the original YOLOv8n model. Regarding tracking ships in SAR images, we developed a multi-object tracking (MOT) technique called Cascaded-Buffered IoU (C-BIoU). This method enlarges the detection and trajectory matching space by increasing the buffer zone, effectively combining detection and trajectory information from short-time sequence SAR images. The findings reveal that our method significantly reduces the computational complexity, parameters, and model size by up to 54.7 %, 68.4 %, and 68.3 %, respectively, with respect to the original model metrics. As a direct consequence of these reductions, our proposed model demonstrates a remarkable 133.1 % improvement in image processing speed expressed as frames per second (FPS). Moreover, Our C-BIoU method shows outstanding performance in tracking accuracy and efficiency, with superior Higher Order Tracking Accuracy (HOTA), Multiple Object Tracking Precision (MOTP), and Identification F1 score (IDF1) scores of 72.8 %, 87.9 %, and 80.7 %, respectively, compared to existing tracking algorithms. The results from testing on multiple datasets highlight our method’s excellent performance in ship detection and tracking, offering high-speed processing capabilities with an average image processing speed of 81 FPS. In this sense, this method provides reliable real-time monitoring and management of maritime traffic, enhancing situational awareness for maritime operations.