Yang Jianing, Liu Xinyuan, Zhang Lingyun, Zhang Li, Yan TingKai, Fu Sheng, Sun Ting, Zhan Haiyang, Xing Fei, You Zheng
Opt Express. 2025 Mar 10;33(5):11301-11316. doi: 10.1364/OE.550513.
Real-time localization and classification of fast-moving objects are crucial in various applications. Traditional imaging approaches face significant challenges, including large data requirements, limited update rates, motion blur, and restrictions in non-visible wavelengths. This paper proposes an image-free method based on complementary single-pixel detection and centralized geometric moments, which effectively integrates target localization and classification into a unified framework. By employing only four specific illumination patterns, the method can simultaneously determine the centroid position and shape of the target at an update rate of up to 5.55 kHz. Theoretical simulations verify the robustness of the proposed method under similarity transformations. Experimental results indicate that the proposed system achieves accurate real-time target localization and classification under diverse conditions, with an RMSE for centroid localization below 0.5 pixels and 93.3% classification accuracy for 30 different objects. The proposed method demonstrates strong adaptability to complicated environments. It holds significant potential for applications in target tracking, character recognition, industrial automation, and the development of optoelectronic neural networks for advanced optical computing tasks.
快速移动目标的实时定位与分类在各种应用中至关重要。传统成像方法面临重大挑战，包括数据需求量大、更新速率有限、运动模糊以及非可见光波长方面的限制。本文提出了一种基于互补单像素检测和集中几何矩的无图像方法，该方法有效地将目标定位和分类集成到一个统一框架中。通过仅采用四种特定照明模式，该方法能够以高达5.55千赫兹的更新速率同时确定目标的质心位置和形状。理论模拟验证了所提方法在相似变换下的鲁棒性。实验结果表明，所提系统在各种条件下均能实现准确的实时目标定位和分类，质心定位的均方根误差低于0.5像素，对30种不同目标的分类准确率为93.3%。所提方法对复杂环境具有很强的适应性。它在目标跟踪、字符识别、工业自动化以及用于先进光学计算任务的光电神经网络开发等应用中具有巨大潜力。
