基于CAM-YOLOX的大场景SAR图像近岸场景舰船目标检测

针对大场景SAR图像近岸场景舰船目标检测中遇到的陆地目标虚警和岸边目标漏检等问题，基于YOLOX设计了一种轻量化的改进模型CAM YOLOX。首先，在骨干部分嵌入CAM，增强舰船特征提取以保持较高的检测性能；其次，在特征金字塔网络结构中增加一个浅层分支，以增强对小目标特征的提取能力；最后，在特征融合网络中用Shuffle unit替换CSPLayer中的CBS和堆叠的Bottleneck结构，实现了模型压缩。在LS-SSDD-v1.0遥感数据集上进行实验，实验结果表明，本文改进算法相较于原始算法在近岸场景舰船检测的精确率P提高了5.51%，召回率R提高了3.68%，模型参数量减小了16.33%。本文算法能在不增加模型参数量的情况下，有效抑制近岸场景中陆地上的虚警和减少岸边舰船漏检率。

Near-shore ship target detection in large scene SAR images based on CAM-YOLOX

A lightweight improved model CAM-YOLOX is designed based on YOLOX to address the issues of false alarms of land targets and missed detections of shore targets encountered in ship target detection in large scene Synthetic Aperture Radar(SAR)images in near-shore scenes. Firstly, embed Coordinate Attention Mechanism in the backbone to enhance ship feature extraction and maintain high detection performance; Secondly, add a shallow branch to the Feature Pyramid Network structure to enhance the ability to extract small target features; Finally, in the feature fusion network, Shuffle unit was used to replace CBS and stacked Bottleneck structures in CSPLayer, achieving model compression. Experiments are carried out on the LS-SSDD-v1.0 remote sensing dataset. The experimental results show that compared with the original algorithm, the improved algorithm in this paper has the precision increased by 5.51%, the recall increased by 3.68%, and the number of model parameters decreased by 16.33% in the near-shore scene ship detection. The proposed algorithm can effectively suppress false alarms on land and reduce the missed detection rate of ships on shore without increasing the number of model parameters.