基于YOLOv5算法的多尺度小目标船舶识别方法

Multi-Scale Small Target Ships Recognition Method Based on YOLOv5 Algorithm

为提高海面多尺度小目标船舶的识别性能,提出一种数据集划分方法,并在YOLOv5算法中改进数据增强方法,融合注意力机制,改进损失函数。实验结果表明,该方法能更好地识别海面上的多尺度小目标船舶,平均精度(mAP)、精确率(P)、召回率(R)分别为99.1%,98.5%,97.5%,识别性能比经典深度学习算法和近几年的方法都高。

A dataset division method was proposed to improve the recognition performance of multi-scale small target ships on the sea surface.The data augmentation method,which contains the YOLOv5 algorithm,was improved by integrating the attention mechanism and improving the loss function.Experimental results showed that this method could better identify multi-scale small target ships on the sea surface,with the average precision(mAP),precision(P)and recall(R)are 99.1%,98.5%and 97.5%,respectively.It suggested that the detection performance was higher than that from classical deep learning algorithms and methods in the past several years.