YOLOv4 与 ORB 深度融合的绝缘子识别定位研究

为了解决复杂背景下铁路接触网绝缘子的快速准确识别及定位问题，提出了一种YOLOv4目标检测算法和ORB特征匹配算法深度融合的绝缘子识别定位方法。首先利用迁移学习的策略训练YOLOv4检测网络，解决了绝缘子数据集样本较少导致过拟合的问题；然后采用高斯金字塔提取图像多尺度特征，使原始ORB算法具备尺度不变性；最后将以上两种算法融合，在双目相机获取的图像上标出绝缘子识别框，并在左右图像识别框内提取特征点进行匹配，利用视差原理还原出绝缘子相对于相机的三维坐标。实验结果表明，该方法可以有效地避免复杂背景干扰，准确地定位出绝缘子的三维坐标，4 m内最大定位误差为2.1%,检测速度为35 fps,具有较高的精确性和实时性。

Research on insulator identification and location based on deep fusion of YOLOv4 and ORB

In order to achieve rapid and accurate recognition and location of insulators for the railway catenary, an insulator recognition and location approach was proposed through deep integrating the YOLOv4 target detection algorithm with the ORB feature matching algorithm under the complicated background. To begin with, the transfer learning strategy was adopted to train the YOLOv4 detection network with an objective of addressing the overfitting problem resulted from few insulator data sets. Then, image multi-scale features were extracted using Gaussian pyramid. By doing so, the original ORB algorithm is equipped with scale invariance; Finally, the insulator recognition frame was marked on the image acquired by a binocular camera by integrating the above two algorithms. On this basis, the three-dimensional coordinates of the insulator relative to the camera can be restored with the parallax principle upon extracting feature points in the left and right image recognition frames for matching. Experimental results demonstrate that the proposed approach featuring high precision and real-time performance can accurately locate the three-dimensional coordinates of the insulator through effectively preventing complex background interference. The maximum positioning error within 4 meters is 2. 1%, and the detection speed is 35 fps.