一种SAR图像舰船检测的YOLOv5-TVC算法

A Ship Detection Algorithm in SAR Images Based on YOLOv5-TVC

为了在SAR图像中准确地检测出大范围海域内的舰船等目标,提出了一种基于YOLOv5-Transformer的目标检测算法:YOLOv5-TVC。首先,使用Vo VNet-57替代原有的CSP-Darknet53以增强对小目标特征的敏感性。其次,将CBAM加在neck的多尺度采样层中,实现深层网络中对重要空间和通道的关注。最后,在Bottleneck-Transformer模块内,用自我注意力机制叠加结构来替代提取特征的卷积层,从而优化对目标特征提取的效率。消融实验和对比实验的结果表明,YOLOv5-TVC检测SAR图像目标的精度优于其他YOLO系列算法。

An improved target detection algorithm based on Yolov5-transformer,YOLOv5-TVC,is proposed to precisely identify ships in a large area from SAR(Synthetic Aperture Radar)images.Firstly,the original CSP-DARKNET53 in YOLOv5 is replaced by VoVNet-57 to increase the sensitivity of small target features.Secondly,a convolutional block attention module(CBAM)is added to the multi-scale sampling layer of neck to realize the attention of important space and channels in deep networks.Finally,in the Bottleneck-Transformer module,the self-attention stack structure is used to replace the feature extraction convolution layer for optimizing efficiency of target feature extraction.The results of ablation experiments and contrast experiments show that the proposed YOLOv5-TVC has a better target detection performance than other YOLO series in precision.