基于区域特征强化的多尺度伪装目标检测方法

doi:10.19678/j.issn.1000-3428.0067588

摘要/Abstract

摘要： 伪装目标检测(COD)能够在复杂环境下探测出与背景相似度极高的伪装目标,在军事侦查和工业检测等领域具有重要的应用价值。针对现有伪装目标检测方法对区域级特征信息利用率低的问题,提出一种基于区域特征强化的多尺度伪装目标检测网络(RFE-Net)方法,实现可见光条件下伪装目标的准确探测。RFE-Net主要包含弱语义特征增强模块、空间信息交互模块和上下文信息聚合模块。首先弱语义特征增强模块引入了条状池化和非对称卷积,通过优化网络的感受野来动态调整搜索区域,从而加强长距离弱语义特征间的联系;然后将级联的U型块结构组合为空间信息交互模块,消除错误预测样本的干扰;最后设计上下文信息聚合模块,通过充分融合深层语义信息和浅层细粒度信息以精细化处理目标边缘细节,从而提升预测准确度。实验结果表明,所提方法能够加强目标内部的弱语义关联,提高目标与背景的区分度,在最大测试集NC4K上的结构性度量、增强对准度量、加权F1值和平均绝对误差4个指标上均取得最优值,其中结构性度量和平均绝对误差高于第2名方法1.1%和7.7%。

关键词: 深度学习, 伪装目标检测, 多尺度融合, 特征强化, 区域级特征

Abstract: Camouflaged Object Detection(COD) can detect camouflaged objects with high similarity to the background in complex environments and is particularly important in military investigation and industrial detection. To address the low utilization of region-level feature information presented by existing COD methods, a multiscale COD method based on a Regional Feature Enhancement Network(RFE-Net) is proposed, which can accurately detect camouflaged objects under visible-light conditions. The RFE-Net includes a weak-semantic-feature enhancement module, spatial-information interaction module, and context-information aggregation module. First, the weak-semantic-feature enhancement module introduces strip pooling and asymmetric convolution to dynamically adjust the search regions by optimizing the receptive field of the network, thereby strengthening the connection between long-range weak semantic features. Subsequently, the cascading U-shaped block structure is combined into a spatial-information interaction module, which eliminates the interference of erroneous prediction samples. Finally, a context-information aggregation module is designed, which significantly improves the prediction accuracy by fully fusing deep semantic information and shallow fine-grained information to refine the object edge details. Experimental results show that the proposed method can strengthen weak semantic associations within an object and improve the distinction between camouflaged objects and the background. On the largest test set, NC4K, the proposed method achieves optimal values for four metrics: structural measure, enhanced alignment measure, weighted F1 value, and mean absolute error. In particular, the structural measure and mean absolute error are 1.1% and 7.7% higher than those of another method, respectively.

Key words: deep learning, Camouflaged Object Detection(COD), multi-scale fusion, feature enhancement, regional features

中图分类号:

TP391

孙帮勇, 马铭, 于涛. 基于区域特征强化的多尺度伪装目标检测方法[J]. 计算机工程, 2024, 50(5): 209-219.

SUN Bangyong, MA Ming, YU Tao. Multi-scale Camouflaged Object Detection Method Based on Regional Feature Enhancement[J]. Computer Engineering, 2024, 50(5): 209-219.

https://www.ecice06.com/CN/Y2024/V50/I5/209

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