基于深度纹理特征的伪装目标边缘细化检测

doi:10.19678/j.issn.1000-3428.0068431

摘要/Abstract

摘要：

为解决传统伪装目标检测(COD)出现的空间信息不完整和目标边界模糊的问题, 提出一种基于深度纹理特征的伪装目标边缘细化检测算法。该算法针对目标的纹理差异和边缘细节设计上下文纹理差异放大模块(CTDAM)、特征边界搜寻模块(FBSM)和边界推理模块(BIM)。CTDAM利用全局感受野覆盖和并行多分支混合卷积方式突出被遮挡的伪装目标的纹理差异; 在注意力特征融合模块(AFFM)中引入局部注意力和位置通道感知并行注意力指导特征跨层融合, 达到平衡局部信息和增强全局上下文语义信息的效果; FBSM利用自注意力机制将低层与高层特征相结合, 处理不同边界像素点之间的依赖关系; BIM利用FBSM所提供的边界指导因子, 指导融合后的特征推断出真实目标并细化边缘细节。在CAMO、CHAMELEON和COD 10K数据集上利用4个客观评估指标进行定量和定性实验, 结果表明, 该算法的检测性能优于对比的8种先进算法, 在COD 10K数据集上, 其平均绝对误差(MAE)达到了0.034。

关键词: 伪装目标检测, 特征边界搜寻, 注意力特征融合, 上下文信息, 纹理差异

Abstract:

To solve the problem of incomplete spatial information and fuzzy target boundaries in conventional Camouflage Object Detection (COD), a COD algorithm based on depth texture features and edge thinning is proposed. Based on the texture difference and edge details of the target, the algorithm designs Context Texture Difference Amplification Module (CTDAM), Feature Boundary Search Module (FBSM), and Boundary Inference Module (BIM). CTDAM uses global receptive field coverage and parallel multi-branch hybrid convolution to highlight the texture differences of occluded camouflage targets. Additionally, it introduces local attention and position channel perception to guide feature cross-layer fusion in Attention Feature Fusion Module (AFFM). Therefore, it achieves the effect of balancing local information and enhancing semantic information of global context. FBSM uses a self-attention mechanism to combine low- and high-level features to deal with the dependence between different boundary pixels; and BIM uses the boundary guidance factor provided by FBSM to guide the fusion features to infer the real target and refine the edge details. Quantitative and qualitative experiments are conducted on the CAMO, CHAMELEON, and COD 10K datasets using four objective evaluation indices. The results demonstrate that the proposed algorithm is superior to other eight algorithms. On the COD 10K dataset, the Mean Absolute Error (MAE) is 0.034.

Key words: Camouflage Object Detection(COD), feature boundary search, attention feature fusion, context information, texture difference

袁昊, 葛海波, 辛世澳, 胥冬梅, 杨雨迪. 基于深度纹理特征的伪装目标边缘细化检测[J]. 计算机工程, 2024, 50(10): 89-99.

YUAN Hao, GE Haibo, XIN Shiao, XU Dongmei, YANG Yudi. Edge Refinement Detection of Camouflage Targets Based on Deep Texture Features[J]. Computer Engineering, 2024, 50(10): 89-99.

https://www.ecice06.com/CN/Y2024/V50/I10/89

图/表 8

图1 　BTNet框架

Fig.1 　The framework of BTNet

图2 　CTDAM模块结构

Fig.2 　CTDAM module structure

图3 　AFFM模块结构

Fig.3 　AFFM module structure

图4 　BIM模块结构

Fig.4 　BIM module structure

图5 各方法定性比较结果

Fig.5 　Qualitative comparison results of each method

图6 各方法效率对比结果

Fig.6 　Results of efficiency comparison of each method

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