YOLO-Based Ship Traffic Monitoring in Fujian Coastal Waters from Sentinel-2 Imagery

Accurate, large-scale maritime traffic monitoring supports marine spatial planning, fishery regulation, and ecological conservation. Medium-resolution optical satellite imagery, such as Sentinel-2A/B, provides a cost-effective complement to incomplete Automatic Identification System (AIS) data. Detecting small vessels in coastal waters remains challenging due to target size, complex backgrounds, and class imbalance. This study presents a robust end-to-end framework for small vessel detection and traffic density mapping using optical remote sensing imagery. A high-quality dataset of 8123 manually annotated vessels was constructed from 14 Sentinel-2 scenes across three marine environments in Fujian, China. An overlapping sliding-window cropping strategy, area truncation filtering, and negative sample preservation improved training efficiency and balance. Experiments compared top-of-atmosphere reflectance with surface reflectance (L2R) from the ACOLITE atmospheric correction (AC) algorithm, showing L2R mitigates aerosol scattering and nearly doubles vessel edge sharpness. YOLO-based detectors were evaluated, with YOLO26m achieving the best localization: F1-score 0.8461, mAP50 0.8979, mAP50–95 0.5536. Using this framework, 1 × 1 km traffic heatmaps for the Fujian coast in 2025 captured seasonal variations influenced by logistics and fishery moratoriums. Results demonstrate that integrating atmospherically corrected imagery with optimized deep learning strategies enhances sub-pixel ship detection, offering a scalable solution for intelligent maritime governance.