Detection of Noisy Laser Spot Based on Multi-scale Neighborhood Search

Laser has been widely used in various areas such as advanced manufacturing and modern healthcare, and played a critical role for accurate detection of laser spot. Considering that the detection is difficult as it is often interfered by the noise light from the background, we propose a new method based on multi-scale neighborhood search (MNS) for laser spot detection in this study. Firstly, a multi-scale difference space for laser spot is constructed, and within this space, a neighborhood search method is designed for estimating the spot center with maximum response. Technically, the extremum point with the maximum response value is searched by comparing neighboring points within a multi-scale space, and the center of the spot is estimated by fitting the local grayscale surface in the image. Then, the region of interest for the spot is extracted, and the directions of the long axis and the short axis are determined by accumulating gradient magnitudes within the region. Also, the length of each axis is obtained by using the second-moment method. The effective performance of the proposed method is verified through simulation experiments, significantly improving the accuracy of spot detection in noisy environments. Meanwhile, the detection errors are consistently low in different noise environments. In particular, under uniform light backgrounds, the average detection accuracy improved by at least 30% compared to the existing methods in the market. Even with natural backlight close to 20% of the peak intensity, the parameter detection errors for laser spot remain below 1%. The results indicate that our method maintains strong robustness in detecting laser spots under various lighting conditions, providing a feasible detection method for laser spots in complex environments.