基于改进YOLOv8s的海面小目标检测算法OA

Target detection in the marine environment is characterized by low image resolution and poor localization accuracy of the model due to the long distance of the imaging equipment,the complexity of the marine environment,and the lack of computational resources.To address above problems,a maritime small target detection algorithm YOLOv8s-Star based on improved YOLOv8s is proposed,aiming to improve the small target detection accuracy without significantly increasing the computational overhead and model complexity.The C2f-Star module is introduced to replace the C2f module in the original backbone network,which enhances the contrast between the small targets and the background,and effectively solves the feature loss problem.The Inverted Residual Mobile Block(iRMB)is introduced to improve the original fast-space pyramid pooling layer,and the model is enhanced to detect the small targets at sea by combining the light-weight convolution and the self-attention mechanism to enhance the model's attention to the detailed features of small targets at sea.The Haar Wavelet Downsampling(HWD)is used to downsample the fused features to improve the model's ability to perceive the edge detail information,and then realize the accurate detection of small targets at sea.The experimental results on the self-built dataset show that mAP@0.50 is improved by 2.6%,and the number of model parameters is reduced by 7.9%,compared with those of baseline models.