基于改进YOLOv5模型的SAR图像舰船目标检测算法

An Improved Algorithm for Detecting Ship Target in SAR Images Based on YOLOv5 Model

针对现有的合成孔径雷达(SAR)图像舰船目标检测算法存在检测精度较差、计算量较大的问题,提出了一种基于YOLOv5和GhostNet的轻量化舰船目标检测算法。引入轻量化网络GhostNet的GhostConv和GhostC3模块用于改进YOLOv5的主干网络,实现大幅减轻模型计算量的目的。在颈部网络引入CBAMC3模块,在特征融合阶段进行注意力调整,实现目标的精确检测。此外,该算法引入EIoU损失函数,提高预测框的回归精度和收敛速度。公开数据集上的测试结果表明,该改进算法在保持较高精度的同时大幅度减少了参数量,并减小了模型体积,是理想的SAR图像轻量化舰船检测模型。

Aiming at the problems of poor detection accuracy and large amount of computation in the existing SAR ship target detection methods,a lightweight ship target detection method based on YOLOv5 and GhostNet is proposed.The GhostConv and GhostC3 modules of the lightweight network GhostNet are introduced to improve the backbone network of YOLOv5,achieving a significant reduction in model computation.The CBAMC3 module is introduced in the neck network to adjust attention during the feature fusion stage and achieve accurate target detection.In addition,the EIoU loss function is introduced to improve the regression accuracy and rate of convergence of the prediction box.The test results on the public dataset indicate that the improved algorithm significantly reduces the number of parameters and model volume while maintaining high accuracy,making it an ideal lightweight ship detection model for SAR images.