ABSTRACT

Automatic ship detection in optical remote-sensing (ORS) images has wide applications in civil and military fields. Research on ship detection in ORS images started late compared to synthetic aperture radar (SAR) images, and it is difficult for traditional image-processing algorithms to achieve high accuracy. Therefore, we propose a ship-detection method based on a deep convolutional neural network that is modified from YOLOv3. We call it fused features and rebuilt (FFR) YOLOv3. We tried some improvements to enhance its performance in ship-detection regions. We added a squeeze-and-excitation (SE) structure to the backbone network to strengthen the ability to extract features. Through a large number of experiments, we optimized the backbone network to improve the speed. We improved the multi-scale detection of YOLOv3 by fusing multi-scale feature maps and regenerating them with a high-resolution network, which can improve the accuracy of detection and location. We used the public HRSC2016 ship-detection dataset and remote-sensing images collected from Google Earth to train, test, and verify our network, which reached a detection speed of about 27 frames per second (fps) on an NVIDIA RTX2080ti, with recall (R) = 95.32% and precision (P) = 95.62%. Experiments show that our network has better accuracy and speed than other methods. In addition, it has strong robustness and can adapt to complex environments like inshore ship detection.

摘要

光学遥感（ORS）图像中的自动舰船检测在民用和军事领域具有广泛的应用。与合成孔径雷达（SAR）图像相比，ORS图像中的舰船检测研究起步较晚，传统图像处理算法很难实现高精度。因此，我们提出了一种基于深度卷积神经网络的舰船检测方法，该方法是从YOLOv3修改而来的。我们称其为融合功能并重建（FFR）YOLOv3。我们尝试了一些改进，以增强其在舰船探测区域的性能。我们在主干网络中添加了挤压和激发（SE）结构，以增强提取特征的能力。通过大量实验，我们优化了骨干网络以提高速度。通过融合多尺度特征图并使用高分辨率网络对其进行再生，我们改进了YOLOv3的多尺度检测，从而可以提高检测和定位的准确性。我们使用了公开的HRSC2016船舶探测数据集和从Google Earth收集的遥感图像来训练，测试和验证我们的网络，该网络在NVIDIA RTX2080ti上达到了约27帧/秒（fps）的检测速度，并且具有召回作用（R）= 95.32％，精度（P）= 95.62％。实验表明，我们的网络比其他方法具有更好的准确性和速度。此外，它还具有很强的鲁棒性，可以适应近海船舶检测等复杂环境。