Adaptive Weight Optimization for Ship Detection

Ship detection is crucial for maintaining maritime sovereignty and monitoring ocean pollution. However, deploying this technology in complex marine environments presents significant challenges, especially when detecting small vessels. Their subtle features, multi-scale variations, and the interference of complex backgrounds often result in poor target localization and classification accuracy in existing models. To address these issues, this paper presents a ship detection model based on multi-modal fusion. The model leverages pre-trained parameters from public datasets to extract features, enhances target identification through a cross-modal synergy mechanism, and introduces an uncertainty loss function to dynamically adjust loss weights, significantly improving detection accuracy across different ship sizes and complex backgrounds. Experimental results on the Levir-Ship dataset, which includes optical remote sensing images, demonstrate the model’s effectiveness with AP , A P _ 50 , A P _ 75 , and AR , scores of 33.7 , 84.8 , 16.1 , and 45.4 , respectively. These results validate the model’s superiority in ship detection, offering strong technical support for maritime surveillance and pollution monitoring, and paving the way for future advancements in marine monitoring technologies.