基于改进YOLOv8n的船舶目标检测算法OA

To address the challenges of missed and false detections in ship image target detection tasks caused by diverse ship target scales and complex background environments,a DSAMPN-YOLOv8n algorithm model for ship target detection was proposed.The model introduces dynamic snake convolution(DSC)and sim-ple parameter-free attention mechanism to enhance the YOLOv8n network's ability to extract and integrate fea-tures in complex environments.Additionally,a multi-scale asymptotic feature pyramid network(AFPN)was constructed to facilitate multi-scale feature exchange,improving feature fusion and enhancing the model's capa-bility to detect ship targets of different scales.The proposed algorithm model was experimentally validated using the Seaships dataset.Results showed that,in comparing with those from the YOLOv8n algorithm,the optimized DSAMPN-YOLOv8n algorithm achieves improvements in precision,recall,mAP@0.5 by 6.0%,6.7%,3.6%,respectively.These findings validate the accuracy and effectiveness of the proposed method.