基于特征与区域定位增强的遥感舰船目标检测

doi:10.19678/j.issn.1000-3428.0065425

摘要/Abstract

摘要：

高分辨率遥感图像在海上监视、海上搜救、海上运输等军用和民用领域的舰船检测方面有着广泛的应用。然而高分辨率光学遥感图像舰船目标检测通常存在背景复杂、目标方向任意、尺度多变等问题，导致检测精度不高。提出一种基于特征和区域定位增强的旋转检测算法RetinaNet-MPD。通过添加一个多尺度特征融合模块，充分融合不同尺度、不同层级的特征信息，以增强不同尺度特征图的特征表示能力。针对复杂背景下的舰船目标检测，提出极化双重注意力网络，通过在注意力网络后加入极化函数，充分提取目标的关键特征，同时抑制不相关信息，以有效区分目标和背景。此外，为更准确地定位舰船目标，在对正负样本进行训练时采用一种动态锚学习方法，从而动态选择目标区域内具有良好定位潜力的高质量锚，提高舰船目标检测精度。实验结果表明，RetinaNet-MPD算法在DOTA舰船和HRSC2016数据集上的检测精度分别为89.3%和85.8%，相比现有旋转目标检测算法的检测精度有所提升。

关键词: 高分辨率遥感图像, 舰船目标检测, 多尺度特征融合, 极化双重注意力网络, 动态锚学习

Abstract:

The use of high-resolution remote sensing imagery for ship detection has a wide range of applications in military and civilian fields, such as maritime surveillance, search and rescue, and transportation.However, in high-resolution optical remote sensing images, complex environment as well as arbitrary directions and variable scales of ship targets lead to poor detection accuracy.To address these limitations, a rotation detection algorithm, known as the RetinaNet-MPD, is proposed based on feature and region localization enhancement.First, the RetinaNet-MPD adds a multi-scale feature fusion module, which entirely integrates feature information at different scales and levels, to enhance the feature representation ability of feature maps at different scales.Second, a Polarized Dual-Attention Network(PDANet) module is proposed for ship target detection in a complex environment.By adding a polarization function after the attention network, the key features of the target are entirely extracted, and irrelevant information is suppressed to effectively distinguish the target from its surrounding.In addition, a Dynamic Anchor Learning(DAL) method is adopted when training the positive and negative samples to dynamically select high-quality anchors with good localization potential in the target region and improve the accuracy and precision of ship target detection.The experimental results show that RetinaNet-MPD algorithm achieved detection accuracy of 89.3% and 85.8% on the DOTA-Ship and HRSC2016 data sets, respectively.Consequently, the average detection accuracy was improved effectively compared with other existing rotating-target detection models.

Key words: high-resolution remote sensing imaging, ship target detection, multi-scale feature fusion, Polarized Dual Attention Network(PDANet), Dynamic Anchor Learning(DAL)

宋志娜, 李莎, 杨建明, 徐川. 基于特征与区域定位增强的遥感舰船目标检测[J]. 计算机工程, 2023, 49(8): 257-264.

Zhina SONG, Sha LI, Jianming YANG, Chuan XU. Remote Sensing Ship Target Detection Based on Feature and Region Localization Enhancement[J]. Computer Engineering, 2023, 49(8): 257-264.

https://www.ecice06.com/CN/Y2023/V49/I8/257

图/表 10

图1 两种检测模式的差别

Fig.1 The difference between two detection modes

图2 RetinaNet-MPD网络结构

Fig.2 Structure of RetinaNet-MPD network

图3 多尺度特征融合模块

Fig.3 Multi-scale feature fusion module

图4 双重注意力网络结构

Fig.4 Structure of dual attention network

图5 极化双重注意力网络的结构

Fig.5 Structure of polarized dual attention network

表1 不同算法在HRSC2016数据集上的检测结果对比

Table 1 Comparison of detection results of different algorithms on the HRSC2016 data set %

算法	AP
R2CNN	73.0
RRPN	78.7
RetinaNet-OBB	81.2
ROI Transformer	85.6
R3Det	88.9
RetinaNet-MPD	89.3

表2 不同算法在DOTA-ship数据集上的检测结果对比

Table 2 Comparison of detection results of different algorithms on the DOTA-ship data set %

算法	AP
R2CNN	55.8
RRPN	57.3
RetinaNet-OBB	79.7
ROI Transformer	82.9
R3Det	84.8
RetinaNet-MPD	85.8

图6 不同算法在DOTA-ship数据集上的检测结果

Fig.6 Detection results of different algorithms on the DOTA-ship data set

表3 RetinaNet-MPD算法中不同模块对检测结果的影响

Table 3 Influence of different modules in RetinaNet-MPD algorithm on detection results %

模块与指标	基线方法	添加不同模块
MFF	—	√	—	—	√	√
PDANet	—	—	√	—	√	√
DAL	—	—	—	√	—	√
AP in HRSC2016	81.2	82.7	83.0	87.3	83.9	89.3
AP in DOTA-ship	79.7	80.8	81.1	83.9	82.2	85.8

图7 本文算法与Baseline算法在HRSC2016数据集上的检测结果

Fig.7 Detection results of algorithm in this paper and Baseline algorithm on HRSC2016 data set

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