High precision measurement is becoming an imperative requirement in many applications. A novel sub-pixel line-edged angle detection method based on convolutional neural network is proposed in this paper. The line edges of targets are accurately estimated by their geometric slope angles with an edge point located on the line. Specifically, the pixel level line-edged images are first obtained by image preprocessing. Then, two separate convolutional neural networks are effectively constructed to boost their discriminative capabilities for the sub-pixel line-edged angle classification. The pixel level line-shaped edge images are used as input and the final network outputs are the specific sub-pixel level line-edged angles. Finally, the sub-pixel level diameter measurements are precisely performed with the estimated angles. Compared with existing methods, the proposed method can estimate the sub-pixel line-edged angle with 0.1 degree accuracy in end-to-end way, even for the noisy images. Simulation results for angle measurement and the real-world experiment for diameter measurement reveal the validity of the proposed method.

中文翻译：

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基于卷积神经网络的子像素线边缘角度检测及其在测量中的应用


高精度测量正在成为许多应用中的迫切要求。提出一种基于卷积神经网络的亚像素线边缘角度检测方法。通过目标的几何倾斜角并在线上定位边缘点，可以准确地估计目标的线边缘。具体地，首先通过图像预处理得到像素级的线边缘图像。然后，有效地构建两个单独的卷积神经网络，以增强其对亚像素线边缘角度分类的判别能力。像素级线形边缘图像作为输入，最终网络输出是特定的子像素级线形边缘角度。最后，使用估计的角度精确执行子像素级直径测量。与现有方法相比，该方法即使对于噪声图像，也可以端到端地以0.1度的精度估计亚像素线边缘角度。角度测量的仿真结果和直径测量的真实实验表明了该方法的有效性。