基于边界特征融合和前景引导的伪装目标检测

刘文犀; 张家榜; 李悦洲; 赖宇; 牛玉贞

doi:10.12263/DZXB.20230668

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基于边界特征融合和前景引导的伪装目标检测

学术论文 | 更新时间：2025-12-24

- 基于边界特征融合和前景引导的伪装目标检测
- Boundary Feature Fusion and Foreground Guidance for Camouflaged Object Detection
- 电子学报 2024年52卷第7期页码：2279-2290
- 作者机构：
  
  福州大学计算机与大数据学院，福建福州 350108
- 作者简介：
  
  [ "刘文犀男，1986年11月出生，福建福州人.福州大学计算机与大数据学院教授、博士生导师.主要研究方向为计算机视觉.E-mail: wenxi.liu@hotmail.com" ]
  [ "张家榜男，1999年1月出生，福建泉州人.福州大学计算机与大数据学院硕士研究生.主要研究方向为计算机视觉、目标检测与分割.E-mail: 1139030617@qq.com" ]
  [ "李悦洲男，1995年11月出生，河北石家庄人.福州大学计算机与大数据学院博士研究生.主要研究方向为图像增强与复原、视觉目标跟踪.E-mail: liyuezhou.cm@gmail.com" ]
  [ "赖宇男，1995年12月出生，福建龙岩人.福州大学计算机与大数据学院硕士研究生.主要研究方向为计算机视觉、目标检测与分割、图像质量评价.E-mail: m1rolai04@gmail.com" ]
  [ "牛玉贞女，1982年7月出生，山东济南人.福州大学计算机与大数据学院教授、博士生导师.主要研究方向为计算机视觉.E-mail: yuzhenniu@gmail.com" ]
- 基金信息：
  
  国家自然科学基金(U21A20472;62072110);国家重点研发计划项目(2021YFB3600503);福建省科技重大专项(2021HZ022007);福建省自然科学基金(2021J01612;2020J01494);福建省科技厅高校产学合作项目(2021H6022)
- DOI：10.12263/DZXB.20230668
  中图分类号： TN911.73;TP391
- 收稿：2023-07-14，
  
  修回：2023-11-10，
  
  纸质出版：2024-07-25
- 稿件说明：
移动端阅览
刘文犀, 张家榜, 李悦洲, 等. 基于边界特征融合和前景引导的伪装目标检测[J]. 电子学报, 2024, 52(07): 2279-2290.

LIU Wen-xi, ZHANG Jia-bang, LI Yue-zhou, et al. Boundary Feature Fusion and Foreground Guidance for Camouflaged Object Detection[J]. Acta Electronica Sinica, 2024, 52(07): 2279-2290.
刘文犀, 张家榜, 李悦洲, 等. 基于边界特征融合和前景引导的伪装目标检测[J]. 电子学报, 2024, 52(07): 2279-2290. DOI：10.12263/DZXB.20230668

LIU Wen-xi, ZHANG Jia-bang, LI Yue-zhou, et al. Boundary Feature Fusion and Foreground Guidance for Camouflaged Object Detection[J]. Acta Electronica Sinica, 2024, 52(07): 2279-2290. DOI：10.12263/DZXB.20230668

摘要

伪装目标检测旨在检测隐藏在复杂环境中的高度隐蔽物体，在医学、农业等多个领域有重要应用价值.现有方法结合边界先验过分强调边界区域，对伪装目标内部信息的表征不足，导致模型对伪装目标的内部区域检测不准确.同时，已有方法缺乏对伪装目标前景特征的有效挖掘，使背景区域被误检为伪装目标.为解决上述问题，本文提出一种基于边界特征融合和前景引导的伪装目标检测方法，该方法由特征提取、边界特征融合、主干特征增强和预测等若干个阶段构成.在边界特征融合阶段，首先，通过边界特征提取模块获得边界特征并预测边界掩码；然后，边界特征融合模块将边界特征和边界掩码与最低层次的主干特征有效融合；同时，加强伪装目标边界位置及内部区域特征.此外，设计前景引导模块，利用预测的伪装目标掩码增强主干特征，即将前一层特征预测的伪装目标掩码作为当前层特征的前景注意力，并对特征执行空间交互，提升网络对空间关系的识别能力，使网络关注精细而完整的伪装目标区域.本文在4个广泛使用的基准数据集上的实验结果表明，提出的方法优于对比的19个主流方法，对伪装目标检测任务具有更强鲁棒性和泛化能力.

Abstract

Camouflage object detection aims to detect highly concealed objects hidden in complex environments

and has important application value in many fields such as medicine and agriculture. The existing methods that combine boundary priors excessively emphasize boundary area and lack the ability to represent the internal information of camouflaged objects

resulting in inaccurate detection of the internal area of the camouflaged objects by the model. At the same time

existing methods lack effective mining of foreground features of camouflaged objects

resulting in the background area being mistakenly detected as camouflaged object. To address the above issues

this paper proposes a camouflage object detection method based on boundary feature fusion and foreground guidance

which consists of several stages such as feature extraction

boundary feature fusion

backbone feature enhancement and prediction. In the boundary feature fusion stage

the boundary features are first obtained through the boundary feature extraction module and the boundary mask is predicted. Then

the boundary feature fusion module effectively fuses the boundary features and boundary mask with the lowest level backbone features

thereby enhancing the camouflage object’s boundary position and internal region features. In addition

a foreground guidance module is designed to enhance the backbone features using the predicted camouflage object mask. The camouflage object mask predicted by the previous layer of features is used as the foreground attention of the current layer features

and performing spatial interaction on the features to enhance the network’s ability to recognize spatial relationships

thereby enabling the network to focus on fine and complete camouflage object areas. A large number of experimental results in this paper on four widely used benchmark datasets show that the proposed method outperforms the 19 mainstream methods compared

and has stronger robustness and generalization ability for camouflage object detection tasks.

关键词

Keywords

references

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