CCAI-YOLO: A High-Precision Synthetic Aperture Radar Ship Detection Model Based on YOLOv8n Algorithm

To tackle core challenges in detecting ship targets within synthetic aperture radar (SAR) images—including coherent speckle noise interference, complex background clutter, and multi-scale target distribution—this paper proposes a high-accuracy detection model, CCAI-YOLO. This model is based on the YOLOv8n framework, achieving systematic enhancements through the collaborative optimisation of key components: within the backbone network, the original C2f structure is replaced with the dynamic convolution module C2f-ODConv, improving the model’s extraction capabilities under noisy interference; the C2f-ACmix module is integrated into the neck network, introducing a self-attention mechanism to strengthen global context information modelling, thereby better distinguishing targets from structured backgrounds; the ASFF detection head optimises multi-scale feature fusion, enhancing detection consistency across different-sized targets. Concurrently, the Inner-SIoU loss function further improves bounding box regression accuracy and accelerates convergence. Experimental results demonstrate that on the public datasets SSDD and SAR-Ship-Dataset, CCAI-YOLO achieves consistent improvements over the baseline model YOLOv8n across key metrics including F1 score, mAP50, and mAP50-95. Its overall performance surpasses current mainstream SAR ship detection methods, providing an effective solution for robust and efficient ship detection in complex scenarios.