A novel lightweight YOLO11-based framework for precisely locating diverse ship targets in complex optical remote sensing photographs

Abstract Detecting ship targets in optical remote sensing images is crucial for ensuring the safety of maritime traffic. However, remote sensing ship detection faces challenges such as low resolution, dense distribution, and multi-scale variations. Additionally, complex backgrounds and changing weather conditions further complicate the detection process. To address these challenges, a lightweight remote sensing ship detection architecture derived from YOLO11 is proposed. Firstly, a Fourier convolution dual-domain fusion module is designed to efficiently integrate multi-scale convolution operations in both the spatial and frequency domains, thereby enhancing the model's capacity to recognize ship targets under complex geographical and weather conditions. Furthermore, a partially parallel synergistic attention mechanism is developed to comprehensively model both global and local features, effectively mitigating the difficulty of extracting ship features caused by complex background interference. Finally, a Ship IoU loss function is proposed, which dynamically calculates the loss by focusing on the shape and scale of the ship targets, ensuring that the geometric properties of the targets are better captured by the model. The improved model demonstrates superior detection performance and lower parameter parameters on the HRSC2016 and DOTA1.0 public remote sensing datasets compared to other advanced algorithms.