改进YOLOv5的SAR图像舰船目标检测

近年来针对合成孔径雷达（synthetic aperture radar，SAR）图像中缺乏颜色和纹理细节的舰船检测技术在深度学习领域中得到了广泛研究，利用深度学习技术可以有效避免传统的复杂特征设计，并且检测精度得到极大改善。针对舰船目标检测框具有高长宽比和密集排列问题，提出一种基于改进YOLOv5的目标检测方法。该方法针对舰船目标检测框特点将检测框长宽作为参数进行综合考虑并对损失函数进行曲线优化，并结合坐标注意力机制（coordinate attention，CA），在模型轻量化的同时实现对舰船目标检测的高速与高精度并存。实验结果表明：相比原YOLOv5方法，该方法的检测精度由原来的92.3%提升到96.7%，mAP（mean average precision）指标由原来的92.5%提升到97.2%，明显优于对比方法。通过改进检测框损失函数和特征提取方式，提高对SAR图像中舰船目标的检测效果。

Improved YOLOv5 Ship Target Detection in SAR Image

In recent years, the ship detection technology for the lack of color and texture details in synthetic aperture radar（SAR） images has been extensively studied in the field of deep learning. The use of deep learning technology can effectively avoid traditional complex feature design, and the accuracy of detection is greatly improved. For the problems of high aspect ratio and dense arrangement of ship targets detection, a target detection method based on improved YOLOv5 is proposed. According to the characteristics of ship targets detection, the length and width of detection are taken into comprehensive consideration and the loss function curve is optimized, and the coordinate attention mechanism（CA） is combined to achieve high-speed and high-precision detection of ship targets while the model is lightweight. The experimental results show that：Compared with the original YOLOv5 method, the detection accuracy of this method is increased from 92.3% to 96.7%, the mAP index is increased from 92.5% to 97.2%, which is significantly better than the comparison method. By improving the detection frame loss function and feature extraction methods, the detection effect of ship targets in SAR images is improved.