Guangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing (GDUT), Ministry of Education, Guangzhou 510006, China.
Sensors (Basel). 2023 Apr 11;23(8):3891. doi: 10.3390/s23083891.
Spot detection has attracted continuous attention for laser sensors with applications in communication, measurement, etc. The existing methods often directly perform binarization processing on the original spot image. They suffer from the interference of the background light. To reduce this kind of interference, we propose a novel method called annular convolution filtering (ACF). In our method, the region of interest (ROI) in the spot image is first searched by using the statistical properties of pixels. Then, the annular convolution strip is constructed based on the energy attenuation property of the laser and the convolution operation is performed in the ROI of the spot image. Finally, a feature similarity index is designed to estimate the parameters of the laser spot. Experiments on three datasets with different kinds of background light show the advantages of our ACF method, with comparison to the theoretical method based on international standard, the practical method used in the market products, and the recent benchmark methods AAMED and ALS.
点检测吸引了激光传感器在通信、测量等领域的持续关注。现有的方法通常直接对点图像进行二值化处理，容易受到背景光的干扰。为了减少这种干扰，我们提出了一种称为环形卷积滤波（ACF）的新方法。在我们的方法中，首先利用像素的统计特性搜索点图像中的感兴趣区域（ROI）。然后，根据激光的能量衰减特性构建环形卷积带，并在点图像的 ROI 中进行卷积运算。最后，设计一个特征相似性指数来估计激光点的参数。在三个具有不同背景光的数据集上的实验表明了我们的 ACF 方法的优势，与基于国际标准的理论方法、市场产品中使用的实际方法以及最近的 AAMED 和 ALS 基准方法进行了比较。
