Aiming at the poor accuracy of monocular 3D object detection algorithms caused by the scale differences of objects with different depths in monocular images, a detection algorithm based on fused sampling and depth-scale constraints is proposed. Firstly, to enhance the ability of the sampled features to represent objects at different scales, a multi-scale fusion module (MFM) is constructed. It fuses the sampled features at different levels and scales through hierarchical aggregation and iterative aggregation, thereby improving the ability to extract implicit scale features of the objects. In addition, a depth-scale correlation module (DSCM) is constructed. It uses the linear projection constraint between depth and scale for compensatory scaling of objects at different scales to the same feature level, balancing the model's focus on objects at different distances. Quantitative results based on the KITTI dataset and Waymo dataset show that for both types of datasets, the proposed algorithm improves the overall average accuracy AP_3D by 1.56 percentage points and 3.07 percentage points, respectively, compared to similar algorithms under multiple difficulties, which verifies the effectiveness and generalization of the algorithm. Meanwhile, qualitative results based on the two datasets validate that the algorithm significantly mitigates the impact of the object scale differences on detection performance.