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<p>In this work, a material recognition technology based on the backscattering field of a target with vortex beam illumination is proposed to meet the application requirements of target material recognition and classification in laser detection. Firstly, the characteristics of the backscattering light field of the target with vortex beam illumination are analyzed, and it is proved that the spatial frequency bandwidth of the specklegram increases with the increase of the topological charge of the vortex beam, so that the features of the specklegram will become more abundant. Subsequently, an experimental setup was built to record the backscattering specklegram and establish a dataset for validation. Six typical artificial neural networks (ANNs) were used to achieve the task of target material recognition. With a dataset of 1 000 samples for each of three categories, the recognition accuracy can be up to 96.89%. Finally, a comprehensive evaluation model is established when we consider the factors, including recognition accuracy, model complexity, and training time, and the performances of these ANNs are compared. Among these ANNs, ResNet-18 exhibits superior overall performance. The proposed target material recognition technique paves a new way to multi-dimensional laser detection technology.</p>

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Deep learning material recognition based on backscattering field of target with vortex beam illumination

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Xingling Fu,
  • Xiaoqi Yang,
  • Fan Jia,
  • Wei Lin,
  • Bo Liu

摘要

In this work, a material recognition technology based on the backscattering field of a target with vortex beam illumination is proposed to meet the application requirements of target material recognition and classification in laser detection. Firstly, the characteristics of the backscattering light field of the target with vortex beam illumination are analyzed, and it is proved that the spatial frequency bandwidth of the specklegram increases with the increase of the topological charge of the vortex beam, so that the features of the specklegram will become more abundant. Subsequently, an experimental setup was built to record the backscattering specklegram and establish a dataset for validation. Six typical artificial neural networks (ANNs) were used to achieve the task of target material recognition. With a dataset of 1 000 samples for each of three categories, the recognition accuracy can be up to 96.89%. Finally, a comprehensive evaluation model is established when we consider the factors, including recognition accuracy, model complexity, and training time, and the performances of these ANNs are compared. Among these ANNs, ResNet-18 exhibits superior overall performance. The proposed target material recognition technique paves a new way to multi-dimensional laser detection technology.