面向工业视觉的小尺寸光斑高速亚像素定位方法
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1.中国科学院微电子研究所;2.南京航空航天大学

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TH741;TP391

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国家重点研发项目(No.2022YFB3403800)


A High-Speed Subpixel Localization Approach for Small-Scale Spots in Industrial Vision Applications
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Institute of Microelectronics Chinese Academy of Sciences

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    摘要:

    为解决工业高速视觉测量中微小光斑中心提取存在的精度下降和计算延迟问题,本文提出一种面向小尺寸光斑的高速实时光斑定位方法。首先,在FPGA中设计并构建了基于滑动窗口亮度一致性的ROI提取算法,提升检测速度。然后,设置了距离加权最小二乘拟合法和基于信噪比的自适应权重调节机制相结合的微小光斑中心提取算法,提升了微小光斑在光照变化和噪声条件下的定位鲁棒性,实现了快速高精度的小尺寸光斑中心定位。实验结果表明,该方法在小尺寸光斑提取的测量场景中,光斑中心定位精度优于0.05像素,处理速度提升约36倍,帧率达160帧/s,满足高速相机实时测量对高精度和低延迟的双重要求,显著提升了长距离、小光斑场景下的实时处理能力。

    Abstract:

    To address the issues of accuracy degradation and computational latency in extracting the centers of tiny light spots in high-speed industrial visual measurement, this paper proposes a high-speed real-time light spot localization method for small-sized spots. First, a region of interest (ROI) extraction algorithm based on sliding window brightness consistency was designed and implemented on an FPGA to enhance detection speed. Subsequently, a center extraction algorithm for tiny light spots was developed by integrating a distance-weighted least squares fitting method with an adaptive weighting adjustment mechanism based on signal-to-noise ratio. This approach improves the localization robustness of tiny light spots under varying lighting and noise conditions, enabling fast and high-precision center localization for small-sized spots. Experimental results demonstrate that in measurement scenarios involving small-sized spot extraction, the proposed method achieves a localization accuracy better than 0.05 pixels, with a processing speed approximately 36 times faster and a frame rate of 160 fps. It meets the dual requirements of high precision and low latency for real-time measurement with high-speed cameras, significantly enhancing real-time processing capabilities in long-distance, small-spot scenarios.

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  • 收稿日期:2025-09-08
  • 最后修改日期:2025-09-24
  • 录用日期:2025-10-14
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