用于水下声呐目标检测的弱特征共焦通道调控方法

何梦云; 何自芬; 张印辉; 陈光晨; 张枫

doi:10.37188/CO.2024-0031

用于水下声呐目标检测的弱特征共焦通道调控方法

doi: 10.37188/CO.2024-0031

cstr: 32171.14.CO.2024-0031

昆明理工大学机电工程学院, 云南昆明 650500

基金项目: 国家自然科学基金资助项目（No. 62171206，No. 62061022）

详细信息

作者简介:
何梦云(2000—)，女，云南昆明人，硕士研究生，2022年于天津科技大学机械工程学院获得学士学位，现为昆明理工大学机电工程学院硕士研究生，主要从事图像处理、机器视觉及机器智能方面的研究。E-mail：1184345885@qq.com

何自芬(1976—)，女，山西阳泉人，博士，教授，硕士生导师，2000年、2005年于西安理工大学分别获得学士和硕士学位，2013年于昆明理工大学获得博士学位，主要从事图像处理和机器视觉等方面的研究。E-mail：zyhhzf1998@163.com

张印辉（1977—），男，河北故城人，博士，教授、博士生导师，2000年和2005年于西安理工大学分别获得学士学位和硕士学位，2010年于昆明理工大学获得博士学位，主要从事图像处理、机器视觉及机器智能等方面的研究。E-mail：zhangyinhui@kust.edu.cn

陈光晨（1997—），男，安徽六安人，博士研究生，2020年于安徽工程大学获得学士学位，2023年于昆明理工大学获得硕士学位，现为昆明理工大学机电工程学院博士研究生，主要从事图像处理、机器视觉、深度学习等方面的研究。E-mail：guangchen_c@yeah.net

张枫（1998—），男，江苏南通人，硕士研究生，2021年于淮阴工学院机械与材料工程学院获得学士学位，现为昆明理工大学机电工程学院硕士研究生，主要从事图像处理、机器视觉及深度学习等方面的研究。E-mail：zf1977497475@163.com

中图分类号: TP394.1;TH691.9
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出版历程
- 收稿日期: 2024-02-05
- 修回日期: 2024-03-06
- 录用日期: 2024-04-26
- 网络出版日期: 2024-05-17

Weak feature confocal channel regulation for underwater sonar target detection

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

Funds: Supported by the National Natural Science Foundation of China (No. 62171206, No. 62061022)

More Information

Corresponding author: zyhhzf1998@163.com

摘要

摘要:
声呐图像视觉检测是复杂水域资源勘探和水下异物目标探测领域的重要技术之一。针对声呐图像中小目标存在的特征微弱和背景信息干扰问题，本文提出弱特征共焦通道调控水下声呐目标检测算法。为了提高模型对弱小目标的信息捕获和表征能力，设计弱小目标特征激活策略，并引入先验框尺度校准机制匹配底层语义特征检测分支，以提高小目标检测精度。应用全局信息聚合模块深入挖掘弱小目标的全局特征，避免冗余信息覆盖小目标微弱关键特征。为解决传统空间金字塔池化易忽视通道信息的问题，提出共焦通道调控池化模块，保留有效通道域小目标信息并克服复杂背景信息干扰。实验结果表明，本文所提模型在水下声呐数据集的9类弱小目标识别的平均检测精度达83.3%，相较基准提高了5.5%，其中铁桶、人体模型和立方体检测精度得到显著提高，分别提高24%、8.6%和7.3%，有效改善水下复杂环境中弱小目标漏检和误检问题。
- 弱小目标检测 /
- 水下声呐图像 /
- 全局信息聚合 /
- 共焦通道调控池化
Abstract:
Visual detection of sonar images is a critical technology in complex water resource exploration and underwater foreign object target detection. Aiming at the problems of weak features and background information interference of small targets in sonar images, we propose a weak feature confocal channel modulation algorithm for underwater sonar target detection. First, in order to improve the model's ability to capture and characterize the information of weak targets, we design a weak target-specific activation strategy and introduce an a priori frame scale calibration mechanism to match the underlying semantic feature detection branch to improve the accuracy of small target detection; second, we apply the global information aggregation module to deeply excavate the global features of weak targets to avoid the redundant information from covering the small target's weak key features; finally, in order to solve the problem of traditional spatial pyramid pooling which is easy to ignore the channel information, the confocal channel regulation pooling module is proposed to retain effective channel domain small target information and overcome interference from complex background information. Experiment results show that the model in this paper achieves an average detection accuracy of 83.3% on nine types of weak targets in the underwater sonar dataset, which is 5.5% higher than the benchmark. Among these, the detection accuracy of iron buckets, human body models and cubes is significantly improved by 24%, 8.6%, and 7.3%, respectively, effectively solving the problem of leakage and misdetection of weak targets in complex underwater environment.
- weak target detection /
- underwater sonar images /
- global information aggregation /
- confocal channel modulation pooling

HTML全文

图 1 WFCCMNet模型结构图

Figure 1. WFCCMNet model structure

下载: 全尺寸图片幻灯片

图 2 全局信息聚合模块结构图

Figure 2. Structural diagram of global information aggregation module

下载: 全尺寸图片幻灯片

图 3 共焦通道调控池化模块结构图

Figure 3. Structural diagram of confocal channel regulation pooling module

下载: 全尺寸图片幻灯片

图 4 数据实例分布图

Figure 4. Distribution of data instances and a priori frames

下载: 全尺寸图片幻灯片

图 5 检测结果可视化

Figure 5. Visualisation of detection results

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表 1 改进前后输出分支与先验框对应关系

Table 1. Correspondence between output branches and a priori boxes before and after improvement

改进前		改进后
输出分支	先验框尺度	输出分支	先验框尺度
-	-	P₄	[(116,90),(156,198),(373,326)]
N₃	[(10,13),(16,30),(33,23)]	P₃	[(5,6),(8,14),(15,11)]
N₄	[(30,61),(62,45),(59,119)]	P₂	[(10,13),(16,30),(33,23)]
N₅	[(116,90),(156,198),(373,326)]	P₁	[(30,61),(62,45),(59,119)]

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表 2 目标图像面积占比

Table 2. Target image area percentage

Classes name	Maximum area of target frame(height×width)	Ration
ball	156×142	0.04
circle cage	128×148	0.04
cube	140×164	0.04
cylinder	122×102	0.02
human body	215×216	0.09
metal bucket	168×206	0.07
plane	187×278	0.10
square cage	130×118	0.03
tyre	166×166	0.05

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表 3 改进模块消融实验

Table 3. Improved module ablation experiments

	GFLOPS	mAP50	human body	ball	circle cage	square cage	tyre	metal bucket	cube	cylinder	plane
Baseline	15.8	77.8	81.2	84.9	81.6	78.7	64.8	61.1	84.4	71.8	92.5
+特征激活	18.6	80.1	87.6	82.5	81.1	80.9	64.9	68.8	86.4	77.5	91
+GIAM	19.5	80.9	86.8	83.8	84.1	78.5	64.3	80.1	85.8	74.8	90.3
+CFCRP	23.7	83.3	89.8	85.7	79.1	82.9	73.1	85.1	91.7	71.2	91

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表 4 实验代号与输出分支和先验框尺度对应关系

Table 4. Correspondence of experiment codes with output layers and a priori boxes

实验代号	输出分支	先验框尺度	输出分支	先验框尺度
G₁	-	-	N₄	[(30,61),(62,45),(59,119)]
G₁	N₃	[(10,13),(16,30),(33,23)]	N₅	[(116,90),(156,198),(373,326)]
G₂	P₄	[(5,6),(8,14),(15,11)]	P₂	[(30,61),(62,45),(59,119)]
G₂	P₃	[(10,13),(16,30),(33,23)]	P₁	[(116,90),(156,198),(373,326)]
G₃	P₄	[(10,13),(16,30),(33,23)]	P₂	[(30,61),(62,45),(59,119)]
G₃	P₃	[(5,6),(8,14),(15,11)]	P₁	[(116,90),(156,198),(373,326)]
G₄	P₄	[(30,61),(62,45),(59,119)]	P₂	[(10,13),(16,30),(33,23)]
G₄	P₃	[(5,6),(8,14),(15,11)]	P₁	[(116,90),(156,198),(373,326)]
G₅	P₄	[(116,90),(156,198),(373,326)]	P₂	[(10,13),(16,30),(33,23)]
G₅	P₃	[(5,6),(8,14),(15,11)]	P₁	[(30,61),(62,45),(59,119)]

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表 5 检测分支与先验框组合定量实验结果

Table 5. Results of quantitative experiments on the combination of detection branch and a priori frame

实验代号	GFLOPS	mAP50	human body	ball	circle cage	square cage	tyre	metal bucket	cube	cylinder	plane
G₁	15.8	77.8	81.2	84.9	81.6	78.7	64.8	61.1	84.4	71.8	92.5
G₂	15.9	78.6	88.8	83.7	79.7	82.6	60.6	66.9	87.1	68.5	89.7
G₃	15.9	77.5	77.9	82.3	80.7	72.4	63.9	67.6	86.9	73.7	92.1
G₄	15.9	79.2	79.9	81.7	78.5	75.2	64.4	74.2	87.3	76.6	95.1
G₅	15.9	79.3	89.4	83.2	78.6	83.3	63	71.1	84.3	71.2	90

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表 6 多尺度共焦卷积对比实验

Table 6. Multi-scale confocal convolution comparison experiments

Base Model	共焦卷积核	GFLOPS	mAP50	human body	ball	circle cage	square cage	tyre	metal bucket	cube	cylinder	plane
YOLOv5s +特征激活 +GIAM	-	19.5	80.9	86.8	83.8	84.1	78.5	64.3	80.1	85.8	74.8	90.3
	+1×1	19.3	80.8	85.3	86.1	67.3	85.1	71.3	84.5	89.7	68.8	89.5
	+1×1 +3×3	19.4	81.4	89.4	84.1	78.8	83.3	66.1	87.5	87.1	68.7	87.5
	+1×1 +3×3 +5×5	20.8	80.6	91.8	83.4	75	79.1	67.2	72.3	87.2	80.5	88.8
	+1×1 +3×3 +5×5 +7×7	23.7	83.3	89.8	85.7	79.1	82.9	73.1	85.1	91.7	71.2	91
	+1×1 +3×3 +5×5 +7×7 +9×9	27.6	78.2	85.3	84.9	74	80.2	66.4	78.2	88.6	61.6	84.6

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表 7 空间特征金字塔池化对比实验

Table 7. Spatial feature pyramid pooling comparison experiments

Base Model	空间特征金字塔池化	GFLOPS	mAP50	human body	ball	circle cage	square cage	tyre	metal bucket	cube	cylinder	plane
YOLOv5s +特征激活 +GIAM	+SPPF	19.5	80.9	86.8	83.8	84.1	78.5	64.3	80.1	85.8	74.8	90.3
	+SPP	18.5	81.4	86.1	83.6	81.9	86.8	67.5	73.2	86.4	76.8	90.2
	+SPPFCSPC	23.6	73.9	78.1	79.7	71.6	76.5	60.3	60.4	85.4	62.1	91.2
	+ASPP	25.1	77	78.4	80.1	72.8	77.6	70	76.6	88	61.8	87.9
	+RFB	19.0	80.5	86.8	85.8	78.7	80.4	69.7	80.3	87.2	72.2	83.8
	+SPPCSPC	23.6	75.2	80.3	78.6	73.4	72.8	62.4	71.4	82.4	72.3	83.1
	+CFCRP	23.7	83.3	89.8	85.7	79.1	82.9	73.1	85.1	91.7	71.2	91

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表 8 网络模型对比实验

Table 8. Network model comparison experiment

Model	GFLOPS	mAP50	human body	ball	circle cage	square cage	tyre	metal bucket	cube	cylinder	plane
SSD^[38]	347.1	78.8	89.1	90.9	74.8	76.4	62.3	77.4	81	68.4	89
RetinaNet^[39]	207.9	68.4	89.8	77.5	59.8	74.7	47.4	75.9	78.6	24	87.9
YOLOv5x^[40]	203.9	77.4	76.1	83.8	76.8	72.7	62.3	80	83.8	66.2	95
Faster RCNN^[41]	193.8	71.2	80.4	81.1	77.6	75.7	45	70	81.8	40.4	88.7
YOLOv7^[29]	103.3	60.4	71.5	74.2	57.8	67	48.1	59.8	72.8	35.8	56.4
YOLOv5m^[40]	48	71.3	68.6	81.8	79	61.5	56.7	58.9	85.1	69.3	80.7
DAMO-YOLO^[42]	36	72.7	65.5	81.3	70.2	70.8	38.7	87.8	85.6	71.8	82.2
YOLOv8s^[43]	28.2	81.4	82.3	86.5	81.1	86.6	65	93.7	88.5	53.5	95
YOLOXs^[44]	26.7	82.4	88.1	88	75.9	87.2	71.7	79.3	89.6	71.4	90
YOLOv5s^[40]	15.8	77.8	81.2	84.9	81.6	78.7	64.8	61.1	84.4	71.8	92.5
YOLOv7-tiny^[45]	13.1	64.2	70.3	84.5	46.7	76.8	35.4	66.3	83.3	41.7	72.7
YOLOv3-tiny^[41]	5.57	63.2	65.2	73.6	69.4	63.2	37.5	63.9	76	48	72.1
YOLOv5n^[40]	4.2	72.3	86	79.2	74.5	77.9	59.3	52.8	81.8	58	80.8
WFCCMNet	23.7	83.3	89.8	85.7	79.1	82.9	73.1	85.1	91.7	71.2	91

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