An effective ship detection approach combining lightweight networks with supervised simulation-to-reality domain adaptation

Computer vision-based ship detection using extensively labeled images is crucial for visual maritime surveillance. However, such data collection is labor-intensive and time-demanding, which hinders the practical application of newly built ship inspection systems. Additionally, well-trained detectors are usually deployed on resource-constrained edge devices, highlighting the lowered complexity of deep neural networks. This study proposes a simulation-to-reality (Sim2Real) domain adaptation framework that alleviates the annotation burden and improves ship detection efficiency by a lightweight adaptive detector. Specifically, a proxy virtual environment is established to generate synthetic images. An automated annotation method is introduced for data labeling, creating a large-scale synthetic ship detection dataset termed SSDShips. The dataset comprises 4800 images, 23,317 annotated instances, six ship categories, and various scenarios. A novel multi-level fusion lightweight (MFL) network is developed based on the you only look once version 8 (YOLOv8) framework, referred to as MFL-YOLOv8. MFL-YOLOv8 is pre-trained on the SSDShips and fine-tuned using both realistic and pseudo-realistic data through a hybrid transfer learning strategy to minimize cross-domain discrepancies. The results show that MFL-YOLOv8 reduces model parameters by 20.5% and giga floating-point operations per second by 66.0%, while improving detection performance, compared to the vanilla YOLOv8. Sim2Real adaptation boosts the model generalization in practical situations, reaching mean average precision mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 81.8%, respectively. It also shrinks the size of real-world labeling by 66.4%, achieving superior detection effectiveness and efficiency, compared to existing ship detection methods within the specific domain. Deployed on the NVIDIA Jetson Orin Nano, the proposed method demonstrates reliable performance in edge-oriented ship detection. The SSDShips dataset is available at https://github.com/congliaoxueCV/SSDShips.