The primary factor for train derailing is the turnout’s aberrant geometric location, thus, it is crucial to monitor it in real time to effectively avoid derailing. This work develops a set of online in-situ monitoring systems for crucial turnouts parameters based on binocular vision to meet this need. First, an online self-calibration method based on straightforward labels is proposed to address the issue that the vibration of railroad traffic readily changes the exterior parameters of the visual measuring equipment. Additionally, laser marking is utilized to strengthen the texture characteristics in turnout monitoring features in order to properly detect them. This can resolve the challenging issue of monitoring features locating following the change of imaging viewpoint produced by the swing of the sharp rail. The Gaussian-weighted grayscale center of gravity approach is proposed to extract the center of the light strip for outdoor light interference. Our method successfully overcomes challenges such easy diffuse reflection on the metal surface of the rail and can accurately find monitoring features. Binocular stereo collaboration is accomplished, and then the monitoring of turnout parameters is finally completed by calculating the spatial three-dimensional coordinates of monitoring features. The field real measurement demonstrates that this device has low hardware cost, high resilience, and rapid speed, with an inaccuracy of approximately 0. 3 mm.