Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2248-2255.DOI: 10.11772/j.issn.1001-9081.2021050831
• Multimedia computing and computer simulation • Previous Articles
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:
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:
CLC Number:
Wenjun FAN, Shuguang ZHAO, Lizheng GUO. Ship detection algorithm based on improved RetinaNet[J]. Journal of Computer Applications, 2022, 42(7): 2248-2255.
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》唯一官方网站, 2022, 42(7): 2248-2255.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021050831
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 |
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 |
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 |
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 |
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 |
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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