基于多尺度语义网络的红外舰船目标检测

陈初侠; 丁勇

基于多尺度语义网络的红外舰船目标检测

陈初侠^1,,
丁勇^2, ,

1.
巢湖学院电子工程学院, 安徽巢湖 238000
2.
浙江大学微纳电子学院, 浙江杭州 310000

详细信息

作者简介:
陈初侠（1984-），男，实验师，主要研究方向为数字图像处理。E-mail：feng84chen@163.com

通讯作者:
丁勇（1974-），男，教授，博士生导师，主要研究方向为图像深度分析与质量评价。E-mail：dingyong09@zju.edu.cn

中图分类号: TN219
计量
- 文章访问数: 185
- HTML全文浏览量: 35
- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-05
- 修回日期: 2021-11-29
- 刊出日期: 2022-05-20

Infrared Ship Detection Based on Multi-scale Semantic Network

CHEN Chuxia^1
,,
DING Yong^{2
, ,}

1.
School of Electronic Engineering, Chaohu University, Chaohu 238000, China
2.
School of Micro Nano Electronics, Zhejiang University, Hangzhou 310000, China

摘要

摘要: 为了增强舰船检测的抗干扰性能，本文提出了一种有效且稳定的单阶段舰船检测网络，该网络主要由3个模块组成：特征优化模块，特征金字塔融合模块和上下文增强模块，其中特征优化模块是提取多尺度上下文信息，并进一步细化和增强顶层特征输入特性，增强弱小目标检测性能；特征金字塔融合模块能够生成表征能力更强的语义信息；上下文增强模块则是整合局部和全局特征增强网络特征表达能力，以降低复杂背景对检测性影响，平衡前景和背景的不均衡差异，消除鱼鳞波的影响。为了验证本文所提方法的有效性和鲁棒性，本文对自建的舰船数据集进行了定性定量验证。实验结果表明，相比现有最新基准对比模型，本文所提网络在自建数据集上均达到了最优性能，在不增加复杂度的情况下极大提升了检测精度。
- 目标检测 /
- 红外舰船 /
- 单阶段网络 /
- 金字塔池化 /
- 上下文增强
Abstract: To enhance the anti-jamming performance of ship detection, an effective and stable single-stage ship detection network is proposed in this study. The network is composed of three modules: feature optimization, feature pyramid fusion, and context enhancement modules. The feature optimization module extracts multi-scale context information and further refines the high-level feature input characteristics, to enhance the performance of dim–small object detection. The feature pyramid fusion module can generate semantic information with stronger representation ability. The context enhancement module integrates local and global features to enhance the network feature expression ability, reduce the impact of a complex background on detectability, adjust the imbalance between the foreground and background, and eliminate the impact of scale-wave. To verify the effectiveness and robustness of the proposed method, qualitative and quantitative verifications are performed on a self-built dataset. Experimental results show that the proposed network achieves optimal performance compared with the latest benchmark comparison model and considerably improves the detection accuracy without increasing complexity.
- object detection /
- infrared ship /
- single-stage network /
- pyramid pooling /
- context enhancement

HTML全文

图 1 复杂红外场景下的船舶示例

Figure 1. Examples of infrared ship in complex infrared scenes

下载: 全尺寸图片幻灯片

图 2 多尺度上下文特征

Figure 2. Framework for multi-scale context feature

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图 3 数据集示例

Figure 3. Example for self-built dataset

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图 4 不同算法定性结果对比

Figure 4. Comparison of qualitative results for different algorithms

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图 5 不同场景下的工程验证结果

Figure 5. Analysis of engineering results for different scenarios

下载: 全尺寸图片幻灯片

表 1 不同模块的消融结果

Table 1. Ablation results of different modules

MCI	SI	Fusion	P	mAP	R	F₁
			71.1	76.3	82.7	86.5
√			74.5	76.9	83.2	86.6
√	√		78.2	78.2	83.5	87.2
√	√	√	80.5	79.2	85.0	88.8

下载: 导出CSV

表 2 自建数据集上的检测结果对比

Table 2. Comparison of results on non-public data sets

Models	P	mAP	R	F₁
YOLOv3	75.5	74.2	81.3	83.9
RetinaNet	77.3	80.6	78.9	77.4
RefineNet	78.4	83.1	79.3	81.1
CenterNet	77.1	78.6	84.5	88.7
FCOS	78.7	85.1	76.6	86.5
Ours	80.5	79.2	85.0	88.8

下载: 导出CSV

表 3 不同数据子集上的检测结果对比

Table 3. Comparison results for different sub-set

Models	SOS				CBC				Others
Models	P	mAP	R	F₁	P	mAP	R	F₁	P	mAP	R	F₁
YOLOv3	67.3	67.4	70.6	68.9	72.1	80.6	83.0	88.3	76.4	85.1	81.5	76.0
RetinaNet	66.6	70.3	72.5	69.4	75.4	81.1	83.1	83.3	78.5	84.5	85.8	76.6
RefineNet	64.8	78.8	78.3	70.9	73.7	82.3	85.0	89.1	77.2	89.6	86.4	75.7
CenterNet	67.8	74.6	79.6	73.2	73.6	77.1	81.9	93.0	79.3	78.9	80.1	85.5
FCOS	64.8	80.8	78.3	70.9	72.5	76.3	82.4	86.2	78.7	77.7	78.5	85.9
Ours	68.0	83.3	83.6	74.9	73.9	85.2	84.9	90.1	83.5	85.4	87.6	86.0

下载: 导出CSV

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