The commonly used monocular vision-based vehicle 3D detection method at present combines object detection with geometric constraint. However， the position of the vanishing point in the geometric constraint has a significant impact on the results. To obtain more accurate constraint conditions， a 3D vehicle detection algorithm based on horizon line detection was proposed. First， the relative position of the vanishing point was obtained using the vehicle image， and the vehicle image was preprocessed to an appropriate size. Then， the preprocessed vehicle image was fed into a vanishing point detection network to obtain a set of heatmaps indicating the vanishing point information. The vanishing point information was regressed， and the horizon information was calculated. Finally， geometric constraint was constructed based on the horizon line information， and the initial dimensions of the vehicle were iteratively optimized within the constrained space to calculate the precise 3D information of the vehicle. The experimental results demonstrate that the proposed horizon line solving algorithm obtains more accurate horizon lines. Compared to the random forest method， there is an AUC （Area Under Curve） improvement of 1.730 percentage points. Simultaneously， the introduced horizon line constraint effectively restricts the 3D vehicle information， resulting in an average precision improvement of 2.201 percentage points compared to the algorithm using diagonal and vanishing point constraint. It can be observed that the horizon line serves as a geometric constraint for solving vehicle 3D information in the context of roadside monocular camera perspectives.