基于深度可分离卷积与通道注意力机制的枪击残留物高光谱图像检测

王嘉驹; 李云鹏; 包佳鑫; 武连全; 代雪晶; 张洪国

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基于深度可分离卷积与通道注意力机制的枪击残留物高光谱图像检测

光学图像处理 | 更新时间：2026-07-23

- 基于深度可分离卷积与通道注意力机制的枪击残留物高光谱图像检测
- Hyperspectral gunshot residue detection based on depth-separable convolution and channel attention mechanism
- 光学技术 2026年52卷第4期页码：500-507
- 作者机构：
  
  中国刑事警察学院，辽宁沈阳 110854
- 作者简介：
  
  王嘉驹（2002— ），男，硕士研究生，从事物证的智能识别方面的研究。3160046662@qq.com
  李云鹏（1988— ），男，副教授，博士，硕士生导师，从事生物特征识别技术、现场物证识别技术、警用人工智能技术方面的研究。lypciomp@126.com
- 基金信息：
  
  国家自然科学基金青年科学基金(62305397);公安部科技计划项目(2025JSYJC11);沈阳市中青年科技创新人才支持计划项目(RC220488);辽宁省“兴辽英才计划”项目(XLYC2403073);中国刑事警察学院重点攻关项目(D2025004);辽宁省应用基础研究计划项目(2022JH2/101300144);辽宁省研究生教育教学改革研究项目(LNYJG2023316);辽宁省教育科学“十四五”规划课题(JG22DB685)
- DOI：
  中图分类号： O433.4
- 收稿：2026-03-04，
  
  修回：2026-05-16，
  
  录用：2026-05-20，
  
  网络首发：2026-07-23，
  
  纸质出版：2026-07-20
- 稿件说明：
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王嘉驹,李云鹏,包佳鑫等.基于深度可分离卷积与通道注意力机制的枪击残留物高光谱图像检测[J].光学技术,2026,52(04):500-507.

WANG Jiaju,LI Yunpeng,BAO Jiaxin,et al.Hyperspectral gunshot residue detection based on depth-separable convolution and channel attention mechanism[J].OPTICAL TECHNIQUE,2026,52(04):500-507.
王嘉驹,李云鹏,包佳鑫等.基于深度可分离卷积与通道注意力机制的枪击残留物高光谱图像检测[J].光学技术,2026,52(04):500-507. DOI：

WANG Jiaju,LI Yunpeng,BAO Jiaxin,et al.Hyperspectral gunshot residue detection based on depth-separable convolution and channel attention mechanism[J].OPTICAL TECHNIQUE,2026,52(04):500-507. DOI：

摘要

在安检与安防现场，复杂纺织物背景下枪击残留物（GSR）的高光谱检测受光谱冗余、噪声干扰与样本不均衡等因素影响，难以实现快速、无损、精准识别。为此，构建了六类不同材质纺织物表面的GSR高光谱数据集，并提出一种融合二维‑三维深度可分离卷积与通道注意力机制的轻量化检测网络（Hybrid DSCDSENet）。方法采用三层级处理框架：首先运用三维深度可分离卷积提取跨波段关联特征，再通过二维深度可分离卷积细化空间结构，最后引入通道注意力机制自适应增强有效谱段、抑制噪声与冗余信息，并结合小波软阈值对输入数据进行预处理。实验结果表明，所提模型在测试集上总体识别精度达到98.55%，相比3D-Res-CNN与RSSAN等典型方法分别提升16.9%和4.91%，在参数量与计算复杂度显著降低的同时，具备更好的鲁棒性与场景泛化能力，为复杂勘查环境中GSR的现场快速识别提供了可行的技术途径。

Abstract

At the security inspection and security protection sites

the high-spectrum detection of gunshot residues （GSR） in complex textile backgrounds is affected by factors such as spectral redundancy

noise interference and sample imbalance

making it difficult to achieve rapid

non-destructive and accurate identification. Therefore

a high-spectrum data set of GSR is constructed on the surfaces of six different types of textile materials

and proposes a lightweight detection network （Hybrid DSCDSENet） that integrates two-dimensional-three-dimensional depth separable convolution and channel attention mechanism. This method adopts a three-level processing framework: first

three-dimensional depth separable convolution is used to extract cross-band correlation features; then

two-dimensional depth separable convolution is used to refine the spatial structure; finally

the channel attention mechanism is introduced to adaptively enhance the effective spectral bands

suppress noise and redundant information

and combine wavelet soft thresholding to preprocess the input data. Experimental results show that the proposed model achieves a total recognition accuracy of 98.55% on the test set

which is 16.9% and 4.91% higher than typical methods such as 3D-Res-CNN and RSSAN

respectively. While significantly reducing the parameter quantity and computational complexity

it has better robustness and scene generalization ability

providing a feasible technical approach for the rapid on-site identification of GSR in complex investigation environments.

关键词

Keywords

references

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