ABSTRACT

Aiming at the low detection rate and high false alarm in small ship detection in SAR images, we propose a small-scale ship detection algorithm based on convolutional neural network in this paper. First, we redesign the feature extraction network according to the characters of ship targets in SAR images. The modified network can enrich the spatial and semantics information of small ships. Then, we propose the Path Argumentation Fusion Network (PAFN) to improve the fusion of different feature maps. PAFN uses bottom-up and top-down ways to fuse more location information and semantic information. Both these two optimizations can enhance the detection for small ships. We evaluate our model based on the open SAR-Ship-Dataset and Gaofen-3 SAR images. The experiment results show that our method has excellent performance for small ship detection compared with other deep learning models. Our model improves AP by 6.5% and has higher detection efficiency compared with the baseline YOLOv3 model.

摘要

针对SAR图像中小舰船检测的低检出率和高误报，提出了一种基于卷积神经网络的小规模舰船检测算法。首先，根据SAR图像中舰船目标的特征重新设计特征提取网络。改进后的网络可以丰富小型船舶的空间和语义信息。然后，我们提出了路径参数融合网络（PAFN），以改进不同特征图的融合。PAFN使用自下而上和自上而下的方式来融合更多的位置信息和语义信息。这两个优化都可以增强对小型船舶的探测能力。我们基于开放的SAR-Ship-Dataset和Gaofen-3 SAR图像评估我们的模型。实验结果表明，与其他深度学习模型相比，我们的方法在舰船检测中具有出色的性能。与基线YOLOv3模型相比，我们的模型将AP提高了6.5％，并具有更高的检测效率。