电子测量与仪器学报

廖国庆,吴文海,曾鑫鹏.YOLOv4 与 ORB 深度融合的绝缘子识别定位研究[J].电子测量与仪器学报,2022,36(2):131-138

YOLOv4 与 ORB 深度融合的绝缘子识别定位研究

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

DOI：

英文关键词:insulator YOLOv4 ORB transfer learning binocular vision

基金项目:

作者	单位
廖国庆	1.西南交通大学机械工程学院
吴文海	1.西南交通大学机械工程学院
曾鑫鹏	1.西南交通大学机械工程学院

Author	Institution
Liao Guoqing	1.insulator; YOLOv4; ORB; transfer learning; binocular vision
Wu Wenhai	1.insulator; YOLOv4; ORB; transfer learning; binocular vision
Zeng Xinpeng	1.insulator; YOLOv4; ORB; transfer learning; binocular vision

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中文摘要:

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。

英文摘要:

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.

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