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.