Yang Jianing, Chen Ran, Peng Yicheng, Zhang Lingyun, Sun Ting, Xing Fei
Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
Department of Automation, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2025 Sep 20;25(18):5886. doi: 10.3390/s25185886.
Achieving rapid and accurate object classification holds significant importance in various domains. However, conventional vision-based techniques suffer from several limitations, including high data redundancy and strong dependence on image quality. In this work, we present a high-speed, image-free object classification method based on dual-pixel measurement and normalized central moment invariants. Leveraging the complementary modulation capability of a digital micromirror device (DMD), the proposed system requires only five tailored binary illumination patterns to simultaneously extract geometric features and perform classification. The system can achieve a classification update rate of up to 4.44 kHz, offering significant improvements in both efficiency and accuracy compared to traditional image-based approaches. Numerical simulations verify the robustness of the method under similarity transformations-including translation, scaling, and rotation-while experimental validations further demonstrate reliable performance across diverse object types. This approach enables real-time, low-data throughput, and reconstruction-free classification, offering new potential for optical computing and edge intelligence applications.
在各个领域中，实现快速准确的目标分类具有重要意义。然而，传统的基于视觉的技术存在若干局限性，包括高数据冗余和对图像质量的强烈依赖。在这项工作中，我们提出了一种基于双像素测量和归一化中心矩不变量的高速、无需图像的目标分类方法。利用数字微镜器件（DMD）的互补调制能力，所提出的系统仅需五个定制的二进制照明图案即可同时提取几何特征并进行分类。该系统可实现高达4.44 kHz的分类更新速率，与传统的基于图像的方法相比，在效率和准确性方面都有显著提高。数值模拟验证了该方法在相似变换（包括平移、缩放和旋转）下的鲁棒性，而实验验证进一步证明了其在各种目标类型上的可靠性能。这种方法能够实现实时、低数据吞吐量和无需重建的分类，为光学计算和边缘智能应用提供了新的潜力。
