Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid

Ziying Song, L. xilinx Wang, Guoxin Zhang, Caiyan Jia, Jiangfeng Bi, Haiyue Wei, Yongchao Xia, Chao Zhang, Lijun Zhao
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引用次数: 1

Abstract

Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOT A remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.
基于尺度空间金字塔的多方向遥感舰船目标快速检测
遥感图像中的船舶通常呈任意方向排列,体积小,排列密集。因此,现有的目标检测算法无法快速准确地检测船舶。为解决上述问题,提出了一种用于船舶快速检测的轻量目标检测网络。该网络由骨干网络、四尺度融合网络和旋转分支组成。首先,设计并使用轻量级网络单元S-LeanNet构建低计算精度的骨干网。然后,设计一个四尺度特征融合模块,生成一个四尺度特征金字塔,该金字塔包含更多的船舶形状、纹理等特征,同时有利于小型船舶的检测。最后,设计了一种新颖的旋转支路模块,利用平衡L1损失函数和R-NMS进行后处理,实现了旋转包围盒的一步精确定位和回归。实验结果表明,与最新的SCRDet检测方法相比,本文方法在DOT A遥感数据集上的检测精度提高了1.1%,运行速度提高了8倍,能够满足船舶快速检测的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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