This invention discloses a 3D
train detection and positioning method based on ground-based
monocular vision. It eliminates the need for
satellite signals or additional trackside equipment. Through convolutional-trans depth
estimation and pseudo-
point cloud optimization, the absolute relative error of depth
estimation can be reduced to below 0.06. After pseudo-
point cloud optimization, the 3D detection accuracy is improved by more than 20%, achieving a final positioning accuracy of 1 meter, meeting the auxiliary positioning requirements of
train control systems. Furthermore, through view
frustum region extraction and lightweight network design, the
inference frame rate is ≥10fps, meeting the real-time positioning requirements of high-speed trains. In addition, existing ground-based
monocular cameras can be reused, eliminating the need for additional hardware. The deployment and maintenance costs are only 1 / 10 of those of
transponder positioning systems. Overall, this invention provides a novel technical path for
train positioning and can be widely applied to auxiliary positioning systems for conventional and high-speed railways. It is particularly suitable for the intelligent upgrading and transformation of existing lines, possessing extremely high
engineering application value.