针对SSD等算法在合成孔径雷达(SAR)图像舰船小目标以及复杂场景下目标的检测效果不佳问题,提出了一种基于多重连接特征金字塔的舰船目标检测方法。首先,针对图像中小目标舰船的特点,构建了全新的特征提取网络I-VGGNet,以解决小尺寸舰船特征信息的丢失问题;其次,增加了多重连接特征金字塔网络模块,加强舰船高层语义特征与低层定位特征的融合,从而提高网络对于中小尺寸舰船的检测性能;最后,为了解决复杂场景对于舰船目标检测的干扰,在广义交并比损失和焦点损失基础上,构造了一个新的损失函数,从而降低网络对于舰船尺度的敏感性,加速模型的收敛。本文方法在中国科学院SAR图像舰船目标数据集上进行了相关实验,实验结果表明,平均精度达到了94.79%,优于现存的主流检测算法,帧率达到了22 frame/s,满足实时检测的需求,所提方法对复杂场景下不同尺寸的舰船目标的检测展现出了良好的适应性。

Aiming at the poor detection effect of SSD and other algorithms on small ship targets in synthetic aperture radar (SAR) images and complex scenes, this paper proposes a method of ship detection based on a multiply connected feature pyramid network. First, according to the characteristics of small target ships in the image, a new feature extraction network I-VGGNet is constructed to solve the problem of the loss of feature information of small ships. Second, the multi-connection feature pyramid network module is added to strengthen the fusion of high-level semantic features of ships and low-level positioning features so as to improve the detection performance of the network for small and medium-sized ships. Finally, in order to solve the interference of complex scenes on ship target detection, this paper constructs a new loss function based on generalized intersection over union loss and focus loss to reduce the sensitivity of the network to the ship scale and accelerates the convergence of the model. The proposed method is tested in related experiments on the Chinese Academy of Sciences SAR image ship target data set. Experimental results show that the average accuracy reaches 94.79%, which is better than the existing mainstream detection algorithms. The frame rate reaches 22 frame/s, which meets the real-time detection requirements, the proposed method shows good adaptability to the detection of ship targets of different sizes in complex scenarios.