《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (7): 2248-2255.DOI: 10.11772/j.issn.1001-9081.2021050831
• 多媒体计算与计算机仿真 • 上一篇
收稿日期:
2021-05-20
修回日期:
2021-12-15
接受日期:
2021-12-29
发布日期:
2022-03-08
出版日期:
2022-07-10
通讯作者:
赵曙光
作者简介:
凡文俊(1996—),男,湖北天门人,硕士研究生,主要研究方向:人工智能、目标检测基金资助:
Wenjun FAN1, Shuguang ZHAO1(), Lizheng GUO2
Received:
2021-05-20
Revised:
2021-12-15
Accepted:
2021-12-29
Online:
2022-03-08
Published:
2022-07-10
Contact:
Shuguang ZHAO
About author:
FAN Wenjun, born in 1996, M. S. candidate. His research interests include artificial intelligence, target detection.Supported by:
摘要:
目前基于深度学习算法的目标检测技术在合成孔径雷达(SAR)图像船舶检测中取得了显著的成果,然而仍存在着小目标船舶和近岸密集排列船舶检测效果差的问题。针对上述问题,提出了基于改进RetinaNet的船舶检测算法。在传统RetinaNet算法的基础上,首先,将特征提取网络残差块中的卷积改进为分组卷积,以增加网络宽度,从而提高网络的特征提取能力;其次,在特征提取网络的后两个阶段加入注意力机制,让网络更加专注于目标区域,从而提升目标检测能力;最后,将软非极大值抑制(Soft-NMS)加入到算法中,降低算法对于近岸密集排列船舶检测的漏检率。在高分辨率SAR图像数据集(HRSID)和SAR船舶检测数据集(SSDD)上的实验结果表明,所提改进算法对于小目标船舶和近岸船舶的检测效果得到了有效提升,与当前优秀的目标检测模型Faster R-CNN、YOLOv3和CenterNet等相比,在检测精度和速度上更加优越。
中图分类号:
凡文俊, 赵曙光, 郭力争. 基于改进RetinaNet的船舶检测算法[J]. 计算机应用, 2022, 42(7): 2248-2255.
Wenjun FAN, Shuguang ZHAO, Lizheng GUO. Ship detection algorithm based on improved RetinaNet[J]. Journal of Computer Applications, 2022, 42(7): 2248-2255.
数据集 | 船舶目标数量 | 图像尺寸 | 图像数量 | 分辨率/m | |||
---|---|---|---|---|---|---|---|
小目标 | 中等目标 | 大目标 | 高/px | 宽/px | |||
SSDD | 1 529 | 935 | 76 | 190~526 | 214~668 | 1 160 | 1~10 |
HRSID | 9 242 | 7 388 | 321 | 800 | 800 | 5 604 | 0.5~3 |
表1 HRSID数据集与SSD数据集
Tab. 1 HRSID dataset and SSD dataset
数据集 | 船舶目标数量 | 图像尺寸 | 图像数量 | 分辨率/m | |||
---|---|---|---|---|---|---|---|
小目标 | 中等目标 | 大目标 | 高/px | 宽/px | |||
SSDD | 1 529 | 935 | 76 | 190~526 | 214~668 | 1 160 | 1~10 |
HRSID | 9 242 | 7 388 | 321 | 800 | 800 | 5 604 | 0.5~3 |
ResNeXt | GAM模块 | Soft-NMS | AP/% | AP50/% | AP75/% |
---|---|---|---|---|---|
✕ | ✕ | ✕ | 52.3 | 90.0 | 57.4 |
√ | ✕ | ✕ | 53.9 | 91.3 | 58.5 |
√ | √ | ✕ | 55.6 | 92.7 | 59.4 |
√ | ✕ | √ | 55.5 | 92.3 | 60.1 |
√ | √ | √ | 56.1 | 92.8 | 60.7 |
表2 改进算法各模块的消融实验结果
Tab. 2 Ablation experimental results of each module of improved algorithm
ResNeXt | GAM模块 | Soft-NMS | AP/% | AP50/% | AP75/% |
---|---|---|---|---|---|
✕ | ✕ | ✕ | 52.3 | 90.0 | 57.4 |
√ | ✕ | ✕ | 53.9 | 91.3 | 58.5 |
√ | √ | ✕ | 55.6 | 92.7 | 59.4 |
√ | ✕ | √ | 55.5 | 92.3 | 60.1 |
√ | √ | √ | 56.1 | 92.8 | 60.7 |
模型 | 测试时间/s | AP/% | AP50 /% | AP75 /% |
---|---|---|---|---|
RetinaNet | 0.082 | 59.1 | 85.2 | 65.6 |
本文改进算法 | 0.136 | 61.5 | 86.1 | 69.0 |
表3 RetinaNet算法改进前后性能对比
Tab. 3 Performance comparison of RetinaNet algorithm before and after improvement
模型 | 测试时间/s | AP/% | AP50 /% | AP75 /% |
---|---|---|---|---|
RetinaNet | 0.082 | 59.1 | 85.2 | 65.6 |
本文改进算法 | 0.136 | 61.5 | 86.1 | 69.0 |
算法 | 测试时间/s | AP/% | AP50 /% | AP75 /% |
---|---|---|---|---|
YOLOv3 | 0.025 | 46.9 | 87.9 | 46.4 |
SSD | 0.029 | 52.5 | 91.2 | 57.0 |
Faster R-CNN | 0.200 | 55.6 | 90.3 | 63.4 |
Libra R-CNN | 0.060 | 55.4 | 91.6 | 62.0 |
CenterNet | 0.055 | 55.6 | 92.0 | 60.3 |
本文算法 | 0.050 | 56.1 | 92.8 | 60.7 |
表4 不同检测算法的性能对比
Tab. 4 Performance comparison of different detection algorithms
算法 | 测试时间/s | AP/% | AP50 /% | AP75 /% |
---|---|---|---|---|
YOLOv3 | 0.025 | 46.9 | 87.9 | 46.4 |
SSD | 0.029 | 52.5 | 91.2 | 57.0 |
Faster R-CNN | 0.200 | 55.6 | 90.3 | 63.4 |
Libra R-CNN | 0.060 | 55.4 | 91.6 | 62.0 |
CenterNet | 0.055 | 55.6 | 92.0 | 60.3 |
本文算法 | 0.050 | 56.1 | 92.8 | 60.7 |
模型 | 近岸数据集 | 离岸数据集 |
---|---|---|
YOLOv3 | 27.9 | 51.3 |
SSD | 34.6 | 57.1 |
Faster R-CNN | 40.3 | 58.6 |
本文算法 | 41.9 | 59.6 |
表5 不同算法在SSDD数据集的近岸与离岸场景的检测精度对比 (%)
Tab. 5 Detection precision comparison of different algorithms innear-shore and off-shore scenarios of SSDD dataset
模型 | 近岸数据集 | 离岸数据集 |
---|---|---|
YOLOv3 | 27.9 | 51.3 |
SSD | 34.6 | 57.1 |
Faster R-CNN | 40.3 | 58.6 |
本文算法 | 41.9 | 59.6 |
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